Table of Contents
Organizing Committee
Advisory Committee
Technical Program Committee
Message from the desk of Chairman, BMIET
Message from the desk of Founder-CEO, BMIET
Message from the desk of Deputy-Director, BMIET
Message from the desk of Principal, BMIET
ABSTRACTS
PaperID Authors Title Page No.
135 Himanshi Saini, Amit Kumar Garg
Performance Analysis of Routing and Protection
Schemes for High Speed Networks
111 Priyanka Aggarwal, Neeraj Kr. Shukla, Simran Choudhary
Efficient CRC Implementation in 10G Ethernet
and DigRF V4 Protocol
133 Riya Sinha, Dr. Amit Kumar Garg, Swati Tyagi
A Comprehensive Review on Comparative Study
of Different Techniques of Dispersion
Compensation
102 Nidhi Sharma, V.K Srivastava, Alok Sharma
An Improved Authentication Security Scheme
109 Vinek Agarwal, J.S Singh, Ashish Negi
Travel-time Prediction: A Short survey
124 Tarun Gupta A survey of Traffic Management and Behavior
in a QoS Environment
130 Namita Kathpal, Amit Kumar Garg
Simulative Investigation of 2.5Gbps RZ modulation format using various optical sources
in SOA based RoF system
131 Mukesh Singhla Energy Efficient Routing Protocols for Wireless
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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10
16
20
26
29
35
40
40
sensor Network
128 Alok Sharma, Nidhi Sharma, V.K Srivastava,
Hundred Percent Secure and Pure Steganography
without Password Protection
136 Abhinav Juneja, Shubham Jain, Ekta Gandhi
Stock Market Data Analysis using Apache Hadoop
115 Abhinav Juneja, Prayans Jain, Siddharth
Generation of Business Intelligence by Sentimental Analysis through Big Data and
Hadoop
123 B.MaheshDynamic Update and Public Auditing with
Dispute Arbitration for Cloud Data
127 Ashima Arya, Jagpreet Sandhu
A Survey on Big Data Storage Issues in Cloud Computing Environment
129 Archna Kumar, Abhinav Juneja, Sapna Juneja
Constraints and Limitations in Software Reliability Prediction
137 Saloni, Vishal Jain, Devender Saini
A Review on the Intelligent Schemes for Automatic Generation control in Modern Power
System
122 Rajeev Kapoor, Jagpreet Singh, Subhash Chander
Internet of Things: A Survey of Architectures and Recent Research Trends
134 Vishal Jain, Saloni, Devender Saini
Strategies in Hybrid Evolutionary Algorithms for
Optimization
138 Gurminder Kaur, Priyansh Gupta
Home and Automobile Automation Model
139 Savita Khatri, Neeraj Dahiya
New Variant of bat algorithm and Clustering
Approach for optimization problems
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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47
50
53
58
62
66
70
73
88
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Organizing Committee
Patrons
Mr Rajeev Jain, Chairman
Mr Rakesh Kuchhal, FOUNDER-CEO
General Chair
Lt Col. Yogesh Jain, DD, BMIET, Sonipat
Technical Program Chair
Prof (Dr) Harish Mittal, Principal, BMIET, Sonipat
Technical Program Co-Chair
Mr. Abhinav Juneja, Vice-Principal
Mr Vishal Jain , HOD, ECE & EEE
Organizing Secretary
Dr Manoj Kumar, BMIET, Sonepat
Organising Committee Members
Dr Seema Dalal
Dr Divya
Mr Sameer Mehta
Ms Monisha
Dr Shilpi Saxena
Ms Sapna Juneja
Mr Sudhir Vasesi
Mr Arun Kumar
Ms Bhawna
Ms Anita Malik
Ms Kanika
Mr Ravinder Kumar
Mr Vishal Verma
Mr Ajay Kumar
Mr Pawan Kumar
Mr Vikas Kuchhal
Mr Sunil Kumar
Mr Sandeep Rathi
Ms Gurminder
Ms Saloni
Ms Sonika
Ms Preeti
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Advisory Committee
• Prof (Dr.) Vinod Kumar, Vice Chancellor, Jaypee University of Information Technology,
Solan, H.P., India
• Brig (Dr) Somnath Mishra, Vice Chancellor, Sikkim Manipal University (SMU), India
• Dr Krzysztof Galkowski, Institute of Control and Computation Engineering, University
of Zielona Gora, Poland
• Dr Venkata Raghavendra Miriampally, Adama Science & Technology University,
Adama, Ethiopia
• Dr A Clementking, College of Computer Science, King Khalid University , Abha, Saudi
Arabia
• Dr Maimunah Mohd Shah, University Technology MARA (UiTM), Puncak Alam
Campus, Selangor, Malaysia
• Dr Alok Tiwari, King AbdulAziz University,Jeddah, Saudi Arabia
• Dr Pranob Misra, LUMILEDs, SANJOSE, United States
• Dr Rajender Singh Chhillar, M. D. University, Rohtak, India
• Dr Amit Kumar Garg, ECE Deptt., Deenbandhu Chhotu Ram University of Sc. & Tech,
Murthal, HR, India
• Dr S P Khatkar, UTD, M D University, Rohtak, India
• Dr V P S Naidu, Multi-Sensor Data Fusion Lab, CSIR - National Aerospace
Laboratories, Bangalore, India
• Dr Vinay Kumar Goel, GNIOT, Gr. Noida, India
• Dr Jyotindra Mulshankerbhai Jani, Lt. M.J. Kundaliya Mahila College, Rajkot, India
• Dr Santosh Kumar Nanda, Eastern Academy of Science and Technology Bhubaneswar,
Odisha, India
• Dr Neeraj K Chavda, A. D. Patel Institute of Technology, New Vallabh Vidyanagar ,
Anand , Gujarat, India
• Dr Geeta R. Bharamagoudar, KLE Institute of Technology, Hubballi, Karnatak, India
• Dr N. Rajkumar, Ramakrishna Engineering College, Vattamalaipalayam,
Coimbatore,Tamilnadu, India
• Dr Santosh K Pandey, Ministry of Electronics & IT, Electronics Niketan, Lodhi Road,
New Delhi, India
• Dr Sunandan Bhunia, Central Institute of Technology, BTAD, Assam, India
• Dr Renu Tuli, Amity School of Engineering and Technology Bijwasan, New Delhi, India
• Dr Sudipto Chaki, MCKV Institute of Engineering, Howrah, West Bengal, India
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Technical Program Committee
Dr Zdzislaw Polkowski Jan Wyzykowski University, Poland
Dr Krzysztof Sozanski University of Zielona Góra, Poland
Dr Xiao-Zhi Gao Lappeenranta University of Technology, Finland
Dr Izzat M Alsmadi Yarmouk University, Jordan
Dr Sampson Asare University of Botswana
Dr Subhasini David CBE, Halhale, Eritrea
Dr Dan Randall American Sentinel University, USA
Dr Veena T. Nandi Majan College, Ruwi, Muscat, Oman
Dr VVR Raman ACHS, Asmara, Eritrea
Dr Samadhiya Durgesh Chung Hua University Taiwan
Dr Kranti V. Toraskar, IS/IT & Info-Security Consultant at KITE-Consult, Hong Kong
Dr Pradeep Bhatia GJUS&T, Hisar
Dr Rahul Rishi UIET, MDU, Rohtak
Dr Dharminder Kumar GJUS&T, Hisar
Dr J S Saini DCRUST, Murthal, India
Dr Sunil Kumar Khatri AIIT, Amity University, Noida
Dr A. K Garg DCRUST, Murthal
Dr Yudhvir Singh GJUS&T, Hisar
Dr. Tanupriya Choudhury ASET, Amity University, Noida
Dr Malay Ranjan Tripathy ASET, Amity Univerty UP, Noida, India
Dr Yumnam Jayanta Singh Assam Don Bosco University, Guwahati, India
Dr Sangeeta Gupta GNIM, Delhi, India
Dr V K Panchal SBIT, Sonepat
Dr Pankaj Gupta VCE, Rohtak
Dr Shyam Akashe ITM, Gwalior
Dr O P Sangwan GJUS&T, Hisar
Dr Poonam Bansal MSIT, New Delhi
Dr Saurabh Mukherjee Banasthali University
Dr A.V. Senthil Kumar Hindusthan College of Arts and Science, Coimbatore
Dr Nirbhay Chaubey ISTAR, VVGT University, Gujarat
Dr Jasmine K S R. V. College of Engineering, Bangalore
Dr Pichai Shanmugavadivu Gandhigram Rural Institute - Deemed University Gandhigram,
Tamil Nadu
Dr Deepak Goyal VCE, Rohtak
Dr N S N Murthy Sharma Sreenidhi Institute of Sc. & Tech. Yamnampet, Hyderabad
Dr Dharmpal Singh JIS College of Engineering, Kalyani, India
Dr N. Rajkumar Sri Ramakrishna Engineering College, Vattamalaipalayam,
Coimbatore
Dr Puneet Goswami SRM University, Sonepat
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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B.M. INSTITUTE OF ENGINEERING & TECHNOLOGY, SONEPAT
Rajiv Jain Chairman
MESSAGE
I am happy to note that B.M. Institute of Engineering & Technology, Sonepat is
organizing its First International Conference on Computational Intelligence &
Communication Technologies (CICT-17) on 4-5th
Nov. 2017.
The conference provides a stage for the academicians and researchers to discuss
the latest developments in the field of Computational Intelligence &
Communication.
My hearty greetings to the faculty members of the Institute, for organizing an
International Conference on an important topic of academic interest. My best
wishes for the successful conduct of the Conference.
Rajiv Jain
Bahalgarh Road, Behind Fazilpur Power Sub Station, Sonepat-131001 (HARYANA)
Tel: 0130-2236911-14, Website: www.bmiet.net E-mail [email protected]
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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B.M INSTITUTE OF ENGINEERING & TECHNOLOGY, SONEPAT
Rakesh Kuchhal FOUNDER-CEO
MESSAGE
I am immensely happy to learn that the Institute is organizing its First International
Conference on Computational Intelligence & Communication Technologies
(CICT-17) on 4-5th
Nov. 2017and a souvenir is being brought to commemorate this
occasion.
I sincerely hope that CICT-2017 is going to deliberate upon several important
topics during the conference which will be of importance to the nation and will
enhance the quality of academic and professional research. I am sure that the
Institute will keep on contributing more effectively in order to promote academic
research.
I convey my best wishes for the success of the Conference
.
Rakesh Kuchhal
Bahalgarh Road, Behind Fazilpur Power Sub Station, Sonepat-131001 (HARYANA)
Tel: 0130-2236911-14, Website: www.bmiet.net E-mail [email protected]
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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B.M. INSTITUTE OF ENGINEERING & TECHNOLOGY, SONEPAT
Lt. Col Yogesh Jain DEPUTY. DIRECTOR
MESSAGE
I am glad to note that B.M Institute of Engineering and Technology, Sonepat is
organizing its First International Conference on Computational Intelligence and
Communication Technologies (CICT-2017) on 4-5th
Nov 2017 on the Institute
campus. Computational Intelligence is the thrust area of all sciences and has
become an indispensable tool in solving the problems of Engineering and
Technology. The Conference will bring like-minded individuals on one platform to
discuss new challenges and trends in field of research. I am sure that the
deliberations will enrich academic wisdom of the participants to enable exploration
of new domains of applications in CICT-17. I hope that the delegates will have an
enjoyable and fruitful stay in the BMIET campus. I wish the Conference a grand
success.
Yogesh Jain
Bahalgarh Road, Behind Fazilpur Power Sub Station, Sonepat-131001 (HARYANA)
Tel: 0130-2236911-14, Website: www.bmiet.net E-mail [email protected]
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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B.M. INSTITUTE OF ENGINEERING & TECHNOLOGY, SONEPAT
Dr. Harish Mittal PRINCIPAL
MESSAGE
I am indeed most delighted to be given the opportunity to chair our First
International Conference on Computational Intelligence and
Communication Technologies (CICT-2017).
As Program chairman of this event, I hope to bring together a good
programme that stimulates knowledge and scientific intellect. A holistic and
interactive approach has been employed in planning the Conference in
which we shall discuss the latest developments in the field of Computational
Intelligence and Communication Technologies.
The review process was a daunting challenge for the Program Committee.
Based on the received review reports, acceptance rate was around 30%.
Specifically, the program covers important aspects of Wired and Wireless
Communications, Simulation & Modeling of Communication Systems,
Computer Vision & Image Processing, Cloud Computing, Artificial,
Biological and Bio-Inspired Intelligence, Antennas and Propagation and
Control Systems.
On behalf of the organizing committee, I would like to extend a warm
invitation to all the participants who have contributed in this Conference.
Dr. Harish Mittal
Bahalgarh Road, Behind Fazilpur Power Sub Station, Sonepat-131001 (HARYANA)
Tel: 0130-2236911-14, Website: www.bmiet.net E-mail [email protected]
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Performance Analysis of Routing and Protection
Schemes for High Speed Networks Himanshi Saini
Assistant Professor, Electronics and Communication
Engineering Department,
Deenbandhu Chhotu Ram University of Science and
Technology, Murthal, Sonepat, Haryana, India
Amit Kumar Garg Professor, Electronics and Communication Engineering
Department,
Deenbandhu Chhotu Ram University of Science and
Technology, Murthal, Sonepat, Haryana, India
Abstract--Management of high-speed networks becomes critical
as traffic volume increases. Impact of failures is aggravated in
networks carrying huge volume of data. Impact is more
pronounced as level of multiplexing in high speed networks
increase. It is important to deal with survivability of high speed
networks in efficient manner in order to maintain high Quality of
Service (QoS). In this paper, a performance analysis of Routing
and Protection algorithms in optical networks has been
performed. Routing algorithm for each routing technique has
been analysed and compared on basis of performance metrics
such as total recovery speed, capacity utilization, blocking
probability, complexity, recovery path length, resource
utilization ratio, cost etc. The present study indicates that
recovery speed of Streams technique is comparable to Dedicated
Path Protection (DPP), Capacity Utilization of streams is better
than DPP and it has least blocking probability among Shared
Path Protection (SPP) and DPP. Among Hamiltonian Cycle
Protection Techniques, Hamiltonian cycle Neighbour capacity
Closure (HNC) offers least spare capacity. The analysis can be
applied to differentiated service selection with optimal Quality of
Service (QoS).
Keywords--optical network; routing; protection; resource
utilization ratio; blocking probability
I. INTRODUCTION
It is observed that a planned survivability algorithm is critical
to minimize damage in high speed networks as data traffic
increases in such networks. Dense Wavelength Division
Multiplexing (DWDM) optical networks has transmission rate
of several gigabits per second so failure for even fraction of
second can result into huge data loss and recovery overhead.
Therefore it is important for a high speed network to be
survivable throughout the operation. Optical opaque networks
are replaced with Transparent Optical Networks (TON) for
high speed. There are various issues in TONs such as
wavelength conversion, exacerbated impact of failure. High
impact of network failure is as a result of huge traffic carried
by TONs. This requires a deep understanding of the trade-offs
between different survivability algorithms [1]. All
performance metrics such as Total Recovery Speed, Capacity
Utilization, Blocking Probability, Complexity, Resource
Utilization Ratio do not respond optimally to a particular
network condition so a tradeoffs among performance
parameters has to be established. Routing algorithms can
optimize bandwidth utilization so that Dense Wavelength
Division Multiplexing (DWDM) users can attain maximum
throughput [2]. Routing and protection techniques such as
Streams, Hamiltonian cycle Protection (HCP), Multi Domain
Hamiltonian Cycle Protection (MHCP) are analyzed on basis
of various network performance parameters.
Section 2 covers the detailed description of algorithms under
consideration and related issues. Section 3 shows the
comparative analysis. Conclusion is presented in section 4
followed by references.
II. ISSUES AND RELATED WORK
Sun-il Kim et al. [3] presented a protection algorithm
STREAMS for single link and node failures. Pre-established
back up path that is shared across different connections is
called a Stream. Streams set up is shown in Fig. 1.Streams
allow sharing of backup wavelengths between backup paths
that are link disjoint and do not diverge whereas in SPP,
wavelength on link [A, B] as shown in Fig. 2, is shared by two
backup paths. Every Streams solution can always be used for
SPP, but not all SPP solutions are applicable to Streams [3].
Non Dynamic streams algorithm initiates with connection
establishment between source and destination pair followed by
finding shortest primary paths, calculation of primary and
backup path pair, checking for validity of found solution,
allocating resources for the new connection and finally
updating network status. Its performance lies between SPP and
DPP. Following are input requirements and output from
Streams. Input for Streams
Output from Stream
P(src,dst) : Set of shortest primary paths (*src=source, *dst=destination)
B(pi,h) :Ordered set of backup paths corresponding to pi ∈P sorted by
length in ascending order (*h=hop)
S :Set of streams [initially empty]
λ(s) : Wavelength used by stream s ∈ S [initially empty]
Free(λ) : Set of free λ-channels on wavelength λ
PSB(s,l) :This set contains all primary paths that are protected by a part
of s, including l
compatible (s, b) :checks for posskble splits/merges that may arise as a
result of adding b to s, w : Shortest possible wavelength
exhop :number of extra hops allowed for backup paths
*A path is treated as an ordered set of links.
Network capacity information, streams information
Update p,b,s
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Fig. 1. Streams Setup [1] Fig. 2. Shared Path Protection Setup (left), Streams Setup (right) [3]
Streams Algorithm
Hong Huanget al. [4] introduced resilience solutions in form
of HCP based Algorithm. Hamiltonian cycle traverses each
node in the network exactly once. HCP configures failed link
as recovery target, not the individual connections [4]. It
reduces spare resources into a minimal set of links.
Hamiltonian cycle based resilience solutions manage and deal
with primary and backup network separately. Performance
metric for moderate Networks is Spare to Primary Ratio (SPR)
which is equal to ratio of spare capacity to primary capacity.
For large networks Hong Huang et al. [4] introduced Single
Hamiltonian resilience for Inhomogeneous networks (SHI)
algorithm, Hamiltonian Cycle Cover (HCC) algorithm and
Hamiltonian cycle Neighbour capacity Closure (HNC)
algorithm.
SHI Algorithm
HCC Algorithm
HNC Algorithm
1. mincost←|links|+1
2. for all pi ∈P(src, dst)
a. for all bj∈B(pi,exhop)
i. for all sk∈S
if (compatible (sk, b))
cost←0
valid←true
for all links lm∈b
if ((p∩PSB(sk,lm)) = not empty),
valid←false
if (lm∉sk) ,cost←cost+1
if (valid AND cost<mincost)
mincost ←cost
stream ←sk
ii. k=k+1, goto i
iii. if (mincost= |links|+1)
stream←b, mincost←length(b)
iv. j=j+1, goto a
v. if (cost<mincost)
mincost ←cost+length(pi), p ←pi, b←bj, s ←stream
b. i=i+1, goto 2
3. all links←b∪s
4. new links←b\s
5. if (new links⊄Free(λ(s)))
a. Free(λ(s)) ←Free(λ(s))∪s
b. a ⊂Free(w)
c. λ(s) ← w
6. for all links lm∈b
a. PSB(s,lm) ←PSB(s,lm)∪p
b. m=m+1, goto 6
7. Free(λ(s)) ←Free(λ(s))\new links
8. s ←all links
HC (bps): The capacity of Hamiltonian Cycle
bwj :capacity of primary link j that is on the HC
bwi :capacity for primary link i that is not on the HC
HC (bps) =max [bwj, 0.5 bwi], over all i, j
From Figure 3: HC (bps) =[5,2,2,5,8/2,6/2]=5
HCC manages network using a multi-domain solution.
1. Find a HC for each domain.
2. For each HC calculate capacity bps =max [bwj, 0.5 bwi]
3. For links that intersect HC’s ,capacity,bpj = max
[bps],where the max operation is taken among
neighboringHCs intersecting on the link j.
4. The result is a protection capacity provision,
whichconsists of a set of HCs, each responsible for
protecting its local domain.
From figure 4: bps =Max[5,4] = 5
1. Partition the primary network into a set of protection
domains.
2. Find a HC for each domain.
3. For each HC calculate capacity bps = max [bwj, 0.5 bwi]
4. For links that intersect HC’s ,capacity, bpj = abs(bps1 -
bps2), where s1 and s2 are the two neighboring
domains and abs( ) is the absolute value function
5. The result is a protection capacity provision, which
consists of a set of HCs, each responsible for protecting
its local domain.
From figure 5: bpj =abs(bps1 - bps2) =abs(5-4) =1
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Fig. 3. Single Hamiltonian resilience for Inhomogeneous networks example
Fig. 4. Hamiltonian Cycle Cover example
Fig. 5. Hamiltonian cycle Neighbour capacity Closure example
Lei Guo et al. [5] proposed MHCP algorithm which considers
Local Hamiltonian Cycle (LHC) in each single-domain to deal
with intra-link failures and Globe Hamiltonian Cycle (GHC)
in multi-domains to protect the inter-link failures. For MHCP
implementation, network model considered in [5] is multi-
domain optical network, (N, λ, InterL, T), where N is the set
of network nodes, λ is the set of wavelengths in each fiber
link, InterL is the set of inter-fiber links between different
domains, and T is the set of topologies of multi-domain
networks defined as T=Nm, IntraLm, m=1,2,... in which Nm
is the set of network nodes in domain m and IntraLm is the set
of intra-fiber links in domain m. In domain m, a Hamiltonian
cycle LHCm which is composed of intra-fiber links is
generated based on the physical topology of domain m to
provide the protection for intra-failures. In multi-domains, a
GHC which is composed of inter-fiber links and virtual-links
is generated based on the virtual topology of multi-domains to
provide the protection for inter-failures, where the virtual-link
VLm is the map of LHCm. All virtual-links compose a set
VLVLm, m=1,2,.... The shortest path algorithm, i.e.,
Dijkstra’s algorithm, is applied to compute the route.
MHCP Algorithm as proposed in [5]
Input: Network topology; T connection requests; rq←0, Output: The total resources consumption R Rq: Connection request r, Wrq: Working path of Rq, Wk: Number of working wavelengths on link k.
Fk: Number of free wavelengths on link k, OLm: Set of on-cycle links which are intra-fiber links traversed by LHCmin
domain m.
SLm: Set of straddling links which are intra-fiber links not traversed by LHCmin domain m, BLm: Number of backup
wavelengths on each on-cycle link on LHCmin domain m, OG : Set of on-cycle links which are inter-fiber links traversed by
GHC in multi-domains, SG : Set of straddling links which are inter-fiber links not traversed by GHC in multi-domains, BG :
Number of backup wavelengths on each on-cycle link on GHC in multi-domains, Costk: Cost of link k, |S |: Number of
elements in set S
1. Backup Wavelengths Assignment
a. Backup wavelengths required on each on-cycle link on LHCm are determined by maximum of four parts
i. Max value of working wavelengths on on-cycle links onLHCm: max (Wk | ∀ K ϵ OLm )
ii. Max value of half of working wavelengths on straddling links on LHCm : max (Wk÷ 2 | ∀ K ϵ
SLm )
iii. Max value of half of working wavelengths on straddling links on GHC: max (Wk ÷ 2 | ∀ K ϵ OG )
iv. Max value of quarter of working wavelengths on straddling links on GHC: max (Wk ÷ 4 | ∀ K ϵ
SG)
b. Backup wavelengths required on each on-cycle link on GHC are determined by maximum of two parts
i. Max value of working wavelengths on on-cycle links on GHC: max (Wk| ∀ K ϵ OG )
ii. Max value of half of working wavelengths on straddling links on GHC: max(Wk ÷ 2 | ∀ K ϵ SG)
2. Working Path Selection
Intra-domain Routing: If the source node X and destination node Y belong to the same domain m. Adjust the cost for
each link and compute least cost working path based on physical topology of domain.
i. If ((k ϵ IntraLm and Fk<1) or ( k∉IntraLm))
ii. Costk = ∞iii. If (k ϵ IntraLm and Fk<1)
iv. Costk = (λ+1- Fk)/λ ×Costk*
v. If the working path traverses link kand the sum of free and backup wavelengths on some on-cycle
link x are not enough : Costk* = ∞
vi. else Costk* = 1
3. Find LHC for each single-domain based on its physical topology
4. Find GHC for multi-domains based on the virtual topology
5. BLm←0 (for every domain m), BG←0
6. Ifrq≥T, go to 10 else goto 5
7. Find Wrq using step 2, If found goto 6 else goto 9
8. Save Wrq, Wk←Wk +1 ∀ K ϵWrq
9. Update BLm(for every domain m) and BG using step 1
10. rq← rq+1, goto 4
11. Block request, rq← rq+1, goto 4
12. Find total resources consumption
R = BG .| OG| + Σ∀ K ϵInterLWk + Σ∀ mBLm. | OLm| (for all domains) + Σ ∀ m Σ ∀ k Wk (for all domains and intra
domain links in each domain)
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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In step 5, in the worst case, MHCP will run three times of
Dijkstra’s algorithm to compute the inter-domain working
path for each connection request. Time complexity of MHCP
is O(3N2 ). In Hamiltonian cycle protection, all working
wavelengths share the common backup wavelengths on
Hamiltonian cycle, while in shared-path protection some
working wavelengths share some backup wavelengths and
other working wavelengths share other backup wavelengths;
that is, the backup wavelengths sharability in Hamiltonian
cycle protection is better than that in shared-path protection
[6].
Sunil Gowda et al. [7] proposed a routing algorithm,
conversion free primary routing (CFPR), converter
multiplexing technique and backup path relocation scheme.
Network Architecture used in [7] is Dynamic routing, Path
level protection; with dedicated and shared protection schemes
for backup paths. Wavelength router architecture is based on
share-per-node wavelength converter configuration. Primary
paths require dedicated resources, including wavelength
converters; it is preferable to route primary connections on
conversion-free paths. Objective of CFPR is to avoid
wavelength conversion while routing primary connections.
Complexity of CFPR is O(N2W), for a network with N nodes
and W wavelengths. Converter Multiplexing Technique
(CMT) Algorithm and Backup Path Relocation Scheme
(BPRS) Algorithm are implemented using a dynamic network
traffic model in which connection requests arrive at a node.
Each connection request is assignment a wavelength. Traffic
load is defined in Erlangs. Share-per-node architecture is used;
all nodes allocate an equal number of converters. Both the
dedicated and shared protection schemes are studied.
In technique presented in [7], first conversion free paths
availability is checked followed by computing primary paths
using Dijkstra’s shortest path algorithm, if conversion free
paths are not available then Hop count based shortest path
routing technique is used, if there are overlapping segments
then Back up path relocation technique is used which is
further implemented in two categories, wavelength relocation
in which new wavelength is used for the overlapping segment
and segment relocation in which completely different path is
relocated to the overlapping segment.Relocation schemes are
illustrated in Fig. 6. For a connection request between nodes 1
and 6, path 1−3−6 on wavelength 0 is computed to be a
candidate primary path. Assigning this path requires hop 1−3
of backup path b1 to be relocated. Wavelength 2 offers a path
between nodes 1 − 3. Thus, the overlapping segment is
relocated to wavelength 2, after reserving a converter at node
1which is illustration of wavelength relocation.Backup path b2
is relocated from path 2 − 3 − 6 onto path 2 − 3 − 5 –6 which
is illustration of segment relocation.
CFPR, CMT, BPRS schemes as presented in [7] are outlined as following:
1. If conversion free paths available AND no path overlapping
a. CFPR
i. Fwx,y= 0, if wavelength w on link (x,y) is assignedto a primary path
ii. Fwx,y=1, if the wavelength is either free or is reserved for backup path(s)
iii. For each wavelength W
iv. Primary routes, Pwx,y are computed using Dijkstra’s shortest path algorithm.
v. If no path is available on w wavelength, Pwx,y= φ
2. Else if (conversion free paths not available)
a. Hop count based shortest path routing algorithm / Convertor multiplexing technique
3. Else if (segment overlapping)
a. Back up path relocation
i. Wavelength Relocation //new wavelength is used for the overlapping segment
Pw: primary path wavelength
Bw: backup path wavelength
Bs: backup path source
Bd: backup path destination
Ps: primary path source
Pd: primary path destination
PwPsPd: set of segments constituting primary path on wavelength w
PwBsBd: set of segments constituting backup path on wavelength w
For w = 1 to W
If any segment s1 ϵ PwPsPd = any segment s2 ϵPw
BsBd
s2 is routed at some available wavelength other than w
ii. Segment Relocation //completely different path is relocated to the overlapping segment
Pw: primary path wavelength
Bw: backup path wavelength
Bs: backup path source
Bd: backup path destination
Ps: primary path source
Pd: primary path destination
PwPsPd: set of segments constituting primary path on wavelength w
PwBsBd: set of segments constituting backup path on wavelength w
For w = 1 to W
If any segment s1 ϵ PwPsPd = any segment s2 ϵPw
BsBd
Relocate s2 on PwBsBd
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Fig. 6. Backup path relocation (left and right sets denote network states before
and after relocation, respectively) [3]
III. RESULTS AND DISCUSSIONS
Shared Path Protection (SPP) allows primary path to share
some of the backup light paths whereas in Dedicated Path
Protection (DPP), primary path has dedicated backup light
path. Streams utilize SPP and DPP merits. Recovery speed of
Streams is comparable to Dedicated Path Protection (DPP),
Capacity Utilization of streams is better than DPP and it has
least blocking probability among SPP and DPP. Comparative
analysis among SPP, Streams and DPP is framed in Table 1.
TABLE I: COMPARISON OF SPP, STREAMS, DPP [1]
Routing and
Protection
Technique
Total Recovery
Speed
Capacity
Utilization
Blocking
Probability
SPP Low High
(Almost Equal)
high
Streams High
(Almost Equal)
least
DPP Low low
Among Hamiltonian Cycle Protection Techniques, HNC
offers least spare capacity, HCP is least complex but
applicable only in moderate size networks with scope of single
failure detection. Comparative analysis among Hamiltonian
Cycle Protection Techniques is framed in Table 2.
TABLE II. COMPARISON OF HCP, SHI, HCC, HNC [4]
Routing and
Protection
Technique
Complexity Scope Recovery path
length
HCP(Moderate
Networks
least Single Failure Long
SHI low
HCC high Multiple
Failure
Short
HNC approachable
to HCC
approachable to
SHI
In Multi domain network, Hamiltonian Cycle offers less
Resource Utilization Ratio (RUR) and less Blocking
Probability (BP) as compared to Shared Protection.The RUR
is defined as the ratio of the total backup wave-lengths over
the total working wavelengths, and smaller RUR means better
resource utilization ratio. The BP is defined as the ratio of the
blocked connection requests over the total connection
requests, and smaller BP means higher network throughput.
Comparative analysis among Shared Protection and
Hamiltonian Cycle Protection in Multi Domain network is
framed in table 3. Comparison of Basic hop-count (HC) based
shortest path routing algorithm and CFPR routing algorithm is
shown in table 4
TABLE III. COMPARISON OF MULTI DOMAIN SHARED
PROTECTION (MSP) AND MULTI-DOMAIN HAMILTONIAN CYCLE
PROTECTION (MHCP) [5]
Routing and
Protection
Technique
RUR (Resource Utilization Ratio)
BP (Blocking Probability)
MSP high high
MHCP low (40% improvement
ratio over MSP)
low (40%
improvement ratio
over MSP)
TABLE IV. COMPARISON OF HC AND CFPR [8]
Routing and
Protection
Technique
Blocking
probability
Connections
blocked due to
wavelength
unavailability
Connections
blocked due
to converter
unavailability
HC High Low High
CFPR Low High Low
IV. Conclusion and Future Scope
Contribution of high speed networks can be efficiently
exploited if appropriate resilience scheme is incorporated in
network design. Network failure under high traffic scenario
result into huge loss of data and revenue. In this paper, some
of the routing techniques like Streams, Hamiltonian Cycle
Protection Techniques, Conversion free Primary Routing,
Multidomain Hamiltonian techniques are analyzed on basis of
performance metrics such as total recovery speed, capacity
utilization, blocking probability, complexity, scope, recovery
path length, applicability, resource utilization ratio, cost. It is
seen that recovery speed of Streams technique is comparable
to Dedicated Path Protection (DPP), Capacity Utilization of
streams is better than DPP and it has least blocking probability
among Shared Path Protection (SPP) and DPP. Among
Hamiltonian Cycle Protection Techniques, Hamiltonian cycle
Neighbour capacity Closure (HNC) offers least spare capacity.
The analysis can select differentiated service along with
maintaining an optimal QoS.
REFERENCES [1] Sun-il Kim, Steven S. Lumetta, “Capacity-Efficient Protection with Fast
Recovery in Optically Transparent Mesh Networks”, First International
Conference on Broadband Networks, pp. 290 – 299, Oct. 2004 [2] Jin Seek Choi, Nada Golmie, Francois Lapeyrere, Frederic Mouveaux,
David Su, “A functional classification of routing and wavelength
assignment schemes in DWDM networks”, CiteSeerXβ
[3] Sun-il Kim, Xiaolan J. Zhang, Steven S. Lumetta, “Rapid and Efficient
Protection for All-Optical WDM Mesh Networks”, IEEE Journal on
Selected Areas in Communications, vol.25 , Issue.9, pp. 68-82, Dec. 2009, doi: 10.1109/JSAC-OCN.2007.026306
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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[4] Hong Huang and John A. Copeland, “A Series of Hamiltonian Cycle
Based Solutions to Provide Simple and Scalable Mesh Optical NetworkResilience”, IEEE Communications Magazine, vol.40, Issue.11, pp. 46-
51, Nov. 2002, doi: 10.1109/MCOM.2002.1046992
[5] Lei Guo , Xingwei Wang, Jiannong Cao, WeigangHou, Hongming Li,Hongpeng Wang, “A New Survivable Heuristic Algorithm Based on
Hamiltonian Cycle Protection in Multi-Domain Optical Networks”,
2009 International Conference on Computer Engineering and
Applications, Singapore, vol.2, 2011
[6] L. Guo, X. Wang, X. Zheng, et al., “New results for path-based shared
protection and link-based Hamiltonian cycle protection in survivable WDM networks”, Photon.Netw.Commun., pp. 245-252, 2008
[7] Sunil Gowda and Krishna M. Sivalingam, “Protection Mechanisms for
Optical WDM Networks based on Wavelength Converter Multiplexing and Backup Path Relocation Techniques”, IEEE Societies INFOCOM
2003. Twenty-Second Annual Joint Conference of the IEEE Computer
and Communications, vol.1, pp. 12-21, 2003.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
15
Efficient CRC Implementation in 10G Ethernet and
DigRF V4 Protocol
Priyanka Aggarwal, Neeraj Kr. Shukla, Simran Choudhary
The NorthCap University, Gurgaon (Haryana) India
Abstract— CRC is a most commonly used error detection
technique in most of the digital logic design, communication
link, so as to confirm whether the received digital message has
got any error or not, and whether it has been corrupted in the
transmission in between the different modules of the design or
not. The protocols or the devices which are required to be
operated at higher speed like 10G Ethernet operates at 156.25
MHz, DigRF V4 (a digital interface standard between
baseband IC and RF IC) operates at 100MHz of clock speed,
there comes the requirement for faster CRC implementation.
Many types of techniques for the same purpose have been
developed starting from serial CRC to parallel CRC with more
and more improvement in parallel CRC by developing
different techniques in the parallel CRC implementation.
There is also a possibility that the packet or the frame in these
protocols does not have length equal to the interface width, in
that case parallel CRC implementation becomes less efficient.
So, there should be a solution for this as well. The proposed
work gives a solution for these two problems by first
implementing the parallel CRC architecture because of its
higher speed as compared to serial CRC, and it also takes care
of the case of packet length not being equal to or a multiple of
the interface width. It uses only three configuration for CRC-
32 to be used as CRC-32 (4), CRC-32 (2) and CRC-32 (1), to
cover all the corner cases of byte presence in the complete
packet, where 4, 2 and 1 are the number of bytes to be
transmitted and are actually present in the packet or frame.
Keywords—10G, CRC, LFSR
I. INTRODUCTION
CRC is one of the most widely used error detection technique in communication protocol, computer networks and many storage devices because of its effectiveness for the same. In this a certain sequence of bits, called checksum are appended to the message which is being transmitted [3] [5]. At the receiver end, it is checked that whether these checksum bits are agreeing with the data or not and thereby ascertain the chances of any error that occurred in the data during the data transfer [10].
The generator basically takes in the input data stream and considers it as an algebraic polynomial and does the modulo-2 division by another polynomial which is actually the binary message string specific for a particular type of protocol or storage device where it is being employed [5].
CRC-N [9] implies that CRC can be defined as CRC-8, CRC-16, CRC-3, etc., where N is the degree of generator polynomial which is used for CRC execution and it is specific for the specific protocol.
CRC-N (m) implies that CRC [11] generator of any defined length (N) can be used in serial or in the parallel manner which depends basically on the number of bits (m) which are being fed in one clock cycle. For any value of N, m=1, then it is called “SERIAL CRC GENERATORS”. In these generators, only one bit is transmitted in one clock cycle. When more than one bit is sent (m>1) with any value of N, then such type of CRC generators are called “PARALLEL CRC GENERATORS” [6] [13].
Mainly two problems are worked for, in this work:
a. Meeting the speed of CRC checking with the system’sspeed.
b. Taking care of the packet length of the frames beingtransmitted.
The RTL Architecture proposed in this work takes care of both the problems in mind.
II. BACKGROUND WORK
The background work in this field deals with first increasing the speed of CRC computation by switching from serial approach to the parallel one. It then revolves around developing newer and more efficient techniques in parallel CRC [8] generators by introducing some DSP algorithm like – Retiming, Unfolding and Pipelining [1], so as to meet thefrequency requirement in high-speed applications. Moreover,a secured LBIST technique is presented for efficient CRCgeneration [2].
A. 10G Ethernet and DigRF V4 Protocol
This section gives brief information about the frequency requirement in 10G Ethernet and DigRF V4 protocols and the CRC width (N) used for them. Both of them are high-speed Protocols, requiring CRC [12] Process to be synchronized with the system’s speed of 156.25 MHz for 10G Ethernet [4] and 100 MHz for DigRF V4 Protocol [2].
The CRC width requirement is different for both protocols. 10G Ethernet requires CRC-32 with 64- bits of data packets and DigRF V4 requires CRC-16 with 32-bits of input data packets. The proposed work finds an optimum solution among serial and parallel CRC Generators to meet the speed of computation while taking care of the actual packet length which may or may not be equal or a multiple of interface width.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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III. PROPOSED HYBRID CRC ARCHITECTURE
The proposed Architecture uses three cases of m values, which are 4, 2 or 1. These three cases cover all the cases of number of bytes that are actually present. It does so by making a valid bit associated with each byte in a packet. The value of this valid-bit ensures whether the particular byte is present or not, and finally the CRC is calculated for the remaining bytes by making use of input width only among 4, 2, or 1.
A. State Machine Diagram and RTL Architecture
Presenting below the State Machine Diagram for theproposed technique of CRC Implementation:
Fig.1. State Machine Diagram for proposed Approach
The Figure shows different cases of how the parallel CRC generators of different widths are being used to generate a CRC of 32-bits for Ethernet and similarly for DigRF V4 Protocol which is of 16-bits.
Fig.2. RTL Architecture of proposed Technique
Figure 1 and 2 are the different ways of showing the
execution of proposed technique which takes the
advantage of parallel CRC generator and takes care of
number of valid bytes. First of all, it checks whether the
last four bytes 0 to 4 are valid (means present) or not. If
yes, then check the status of remaining bytes 4 to 7 to
find how many of these are valid and then the result of
these 0 to 3 bytes is fed to the remaining one. In case
lower four bytes are not valid, it checks whether only two
bytes are valid. If yes, then it checks for the next two
bytes, 2nd and 3rd that whether they are valid or not. If
yes, then it feeds the results of 0th and 1st byte CRC to
these two, otherwise that becomes the result. In this way,
the proposed Architecture for CRC calculation solves the
two problems stated.
IV. SIMULATIONS AND RESULTS
The simulation part covers the CRC-32 generation for 10G Ethernet taking in 64-bits as input packet data, by using three ways: CRC-32 (1), CRC-32 (64) and the proposed one. These are synthesized on Xilinx 14.7 ISE. The synthesis reports are generated for all the three approaches on the same tool.
Following are the Frequency requirement for the CRC-32 = 23ABDBF7, for the input data = FF00FF00FF00FF00, with the three methodologies:
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Fig.3. CRC-32 Value
TABLE 1. Synthesis Report
Technique Used Frequency Requirement
CRC-32 (1) 146.07 MHz
CRC-32 (64) 456.12 MHz
Proposed 173.6 MHz
Fig.4. Differences of Frequencies with the required
As seen from the values, the value in case of Serial CRC-32 is near to as desired (156.25 MHz) for 10G Ethernet, but Parallel one is more efficient in terms of speed compared to any of the three approaches, but still the emphasis is laid on the proposed technique because although it is having less efficiency as compared to parallel approach but it is closer to the required value. Moreover the proposed approach takes care of the number of bytes that are actually present in a packet being exchanged.
Depending upon the value of “cntrl_in [7:0]” variable, the byte presence is being detected. When all the bytes are present, the value of this = 1111111, as shown in the Fig.5 When any of the byte is not there, the corresponding bit sets to “0”. For instance, when MSB is not present and the CRC-32 is generated, then input data = “00FFFF00FF00FF00”, which gives the CRC-32 value = B983C7C4.
0
100
200
300
400
500
CRC-32(1) CRC-32(64) Proposed
Technique
Synthesis Report for 10G Ethernet
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Fig.5. CRC-32 when MSB not Valid
Similar technique is applied on the DigRF V4 Protocol to get the similar results of frequency matching with 100 MHz and in this also the CRC computation is done in accordance with the number of valid bytes present in a packet being exchanged.
REFERENCES
[1] Chaitali Tohgaonkar, Prof. Sanjay B. Tembhurne, Prof. Vipin S. Bhure, “Design of Parallel CRC Generation for High Speed Application”, International Journal of Advanced Research In Computer and Communication Engineering, Vol. 4, Issue 6, pp.265-267, June 2015.
[2] N.Prasad, P.V.V.Rajesh, “A Novel Parallel CRC Generation with Secured LBIST”, International Journal & Magazine of Engineering, Technology, Management and Research, Vol. 3, Issue 4, pp.96-99, April, 2016
[3] Computing.dcu.ie, ‘Polynomial codes for error detection’. [Online]. Available:http://www.computing.dcu.ie/~humphrys/Notes/Networks/data.polynomial.html
[4] Microchip.com, ‘SM843251-156’, Year published 2010. [Online]. Available:http://ww1.microchip.com/downloads/en/DeviceDoc/sm843251-156.pdf
[5] Steven R. King, Frank L. Berry, Michael E. Kounavis, ‘Performing a cyclic redundancy checksum operation responsive to a user-level instruction’, 20170242746, May 9, 2017.
[6] Prof. M. S. Kasar, Gauri Mandhare, Snehal Patil, Preeti Kumari, Sarika Yadav, “FPGA IMPLEMENTATION OF 8-BIT PARALLEL CYCLIC REDUNDANCY CODE”, International Education & Research Journal [IERJ], E-ISSN No: 2454-9916, Vol. 3, Issue 4, April 2017
[7] Qianqi Zhuang, Shawn Patrick Stapleton, ‘Power amplifier protection using a cyclic redundancy check on the digital transport of data’, 15380686, Jan 31, 2017
[8] Mahya Sam Daliri, Reza Faghih Mirzaee, Keivan Navi, Nader Bagherzadeh, “Ternary cyclic redundancy check by a new hardware-friendly ternary operator”, Microelectronics Journal, Volume 54, August 2016.
[9] Weirong Jiang, Gordon J. Brebner, Mark B. Carson, ‘Modular and scalable cyclic redundancy check computation circuit’, WO/2014/144941, May 24, 2016.
[10] Hye Ji KIM, Ji Hoon Kim, ‘Apparatus and method for cyclicredundancy check’, US20160371142 A1, Dec 22 2016.
[11] Philip Koopman, Kevin Driscoll, Brendan Hall, “Selection of Cyclic Redundancy Code and Checksum Algorithms to Ensure Critical Data Integrity”, National Technical Information Service (NTIS),Springfield, Virginia, March 2015
[12] Joong-Ho Lee, Control and Automation (CA), “CRC (Cyclic Redundancy Check) Implementation in High-Speed Semiconductor Memory”, 2015 8th International Conference on on Control and Automation, 25-28 Nov. 2015
[13] Yuanhong Huo, Xiaoyang Li, Wei Wang, Dake Liu, “High performance table-based architecture for parallel CRC calculation”, 2015 IEEE International Workshop, 22-24 April 2015
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
19
A Comprehensive Review on Comparative Study of
Different Techniques of Dispersion Compensation Riya Sinha1, Dr. Amit Kumar Garg2, Swati Tyagi3
1,3M.Tech. Scholar, Department of Electronics and Communication Engineering, DCRUST, Murthal
2Professor, Department of Electronics and Communication Engineering, DCRUST, Murthal
Abstract – Optical fiber communication (OFC) provides
high bit rate data communication. There are various
types of impairments and signal degradation
mechanisms which are involved with this high speed communication system. In case of long distance
communication, the most prominent impairment is
dispersion. Dispersion affects the information signal
very badly when it travels along a long distance. There
are different techniques available to compensate
dispersion. In this paper, the various methods of
dispersion compensation in single mode fiber created
because of dependence of group index to wavelength
known as chromatic dispersion are being discussed.
Various methods of dispersion compensation are
Dispersion compensation fiber (DCF) which
compensates dispersion at 1310nm and 1550 nm and
Fiber Bragg gratings (FBG) which compensate dispersion at wavelength around 1550nm. These
techniques and their performance measures are being
compared with respect to the BER, Q-factor, Eye height,
threshold etc. DCF technique increases the total losses
because of non-linear effects and cost of the optical
transmission system while FBG decreases the cost of the
system and has low insertion loss as well. Based on the simulation results, it has been concluded that which
technique is better for high speed long haul OFC
networks.
Keywords – Dispersion, DCF, FBG, dispersion
compensation, long haul communication.
I. INTRODUCTION
Optical fiber communication is one of the most
dominant topic in the communication system in today’s
era. It not only helps in increasing the transmission
speed but it also decreases the overall cost of the
communication system. But, for the application of
optical fiber in long haul communication, when the
signal is being transmitted at transmitter, some losses are
observed at the receiver end which results in some
information loss from the original data signal. In Single
mode fiber (SMF), chromatic dispersion and polarization mode dispersion takes place. Chromatic
dispersion occurs due to the dependence of speed of the
information carrying signal on the refractive index of the
fiber which further depends on the wavelength of the
signal carrying information. It can be compensated using
different dispersion compensation techniques. Fiber
Bragg Grating (FBG) and Dispersion Compensating
Fiber (DCF) are two most commonly used techniques
for dispersion compensation in long haul
communication. DCF compensation needs very high
negative dispersion coefficient for compensating
dispersion in a narrow band of frequency. This increases
the overall losses from non-linear effects and the cost of the optical communication system. In FBG technique,
the propagated light which satisfies the Bragg condition
is resonated by the grating structure and is reflected and
thus we get only a small part of the signal and the rest all
goes out of the fiber. It gives low losses and also
decreases the cost of the transmission system.
In this paper, the performance analysis between these two techniques is being compared in order to find
the better compensation technique for long distance
optical fiber communication. For a SMF of dispersion
parameter (16 ps/nm/km), a DCF of (-80 ps/nm/km) can
be used to compensate the dispersion. But using DCF
technique would be more useful for 100 km or 288 km
distances. On comparing the Q-factor of the two
compensation techniques, the Q-factor for DCF at 288
km is almost same as the Q-factor of FBG compensation
at 100 km. This means by using DCF, the optical signal
can travel three times more distance than FBG with the
same Q-factor.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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The rest of the paper is organized as follows. The
theory of the dispersion compensation schemes
explained in section II. The results are given in section
III. Concluding remarks are given in section IV.
II. THEORY
Dispersion needs to be compensated by
dispersion compensation techniques. The first type is
DCF or Dispersion Compensating Fiber and the second
type is FBG or Fiber Bragg Grating. DCF and FBG
explained in section A and B.
A. Dispersion Compensating Fiber
In dispersion compensating fiber technique, a fiber
having a large negative dispersion is being used alongwith a standard fiber. The amount of light dispersed by a
normal fiber is reduced or even nullified by using a
dispersion compensating fiber having a very large value
of dispersion of opposite sign as compared to tha
standard fiber. DCF’s are used to upgrade the installed
1310 nm optimized optical fiber links for operations at1550 nm. The higher the dispersion coeff
DCF, the smaller will be required length.
compensation Fibers have a high negative dispersion
from -70 to -90ps/nm.km and can be used to compensate
the positive dispersion of transmitter fiber in C and L
bands.
Methodology:
Fiber based compensation is done by three methods
1. Pre-compensation: In this scheme, the DCF of
negative dispersion is placed before the SMF as shown
in Fig. 1(a).
2. Post-compensation: In this scheme, the DCF of
negative dispersion is placed after the SMF as shown in
Fig. 1(b).
3. Symmetrical or mixed compensation: In this scheme,the DCF of negative dispersion is once placed before
SMF and then placed after SMF as shown in Fig. 1
(a) Pre-compensation scheme
(b) Post-compensation scheme
The rest of the paper is organized as follows. The
dispersion compensation schemes are
given in section
III. Concluding remarks are given in section IV.
Dispersion needs to be compensated by various
The first type is
DCF or Dispersion Compensating Fiber and the second
rating. DCF and FBG are
In dispersion compensating fiber technique, a fiber
negative dispersion is being used along with a standard fiber. The amount of light dispersed by a
normal fiber is reduced or even nullified by using a
dispersion compensating fiber having a very large value
of dispersion of opposite sign as compared to that of the
DCF’s are used to upgrade the installed
1310 nm optimized optical fiber links for operations at 1550 nm. The higher the dispersion coefficient of the
length. Dispersion
igh negative dispersion
90ps/nm.km and can be used to compensate
the positive dispersion of transmitter fiber in C and L
Fiber based compensation is done by three methods –
, the DCF of
ced before the SMF as shown
, the DCF of
aced after the SMF as shown in
ed compensation: In this scheme, rsion is once placed before
n placed after SMF as shown in Fig. 1(c).
(c) Symmetric compensation scheme
Fig. 1: Different dispersion compensation schemes
In this, symmetric compensation method largely reduces
the non-linear effects as compared to pre
and post-compensation method. As the bit error rate
(BER) increases, output of the optical fiber also
increases. Symmetric compensation has minimum bit
error rate indicating the best performance in comparison
to pre and post compensation. Advantages of DCF are
that they can be easily constructed and highly reliable.
DCF provides continuous compensation over a wide
range of optical wavelengths. However DCF has high
insertion loss. A 60 km compensator can exhibit 6 dB of
loss or more. Because of this, DCF's are usually colocated with EDFA's which also increase the overall cost
of the fiber. Since DCF has a small core s
make it prone to certain types of nonlinearities. So DCF
also has high optical nonlinearities.
B. Fiber Bragg Gratings
Fiber Bragg gratings were introduced in 1980 and have
been a subject of research with several applications. It is
a reflective device composed of an optical fiber that
contains a modulation of its core refractive index over a
certain length. The Grating reflects light propagation
through the fiber when its wavelength corresponds to the
modulation periodicity. The reflected wavelen
called the Bragg wavelength, and is defined by the
relationship:
Using fiber Bragg gratings for dispersion
is a promising approach because they are passive optical
component fiber compatible, having low insertion losses
and costs. They are used as sensors, as wavelength
stabilizers for pump lasers, in narrow band WDM
add/drop filters and also as fi
compensation. Advantages of FBG are that it helps in
minimizing the overall cost of the fiber and also it also
has low insertion loss.
λ 2n
where n is the effective refractive index of thegrating in the fiber core and Λ
period.
Symmetric compensation scheme
dispersion compensation schemes
In this, symmetric compensation method largely reduces
linear effects as compared to pre-compensation
compensation method. As the bit error rate
(BER) increases, output of the optical fiber also
Symmetric compensation has minimum bit
error rate indicating the best performance in comparison
to pre and post compensation. Advantages of DCF are
that they can be easily constructed and highly reliable.
DCF provides continuous compensation over a wide
nge of optical wavelengths. However DCF has high
insertion loss. A 60 km compensator can exhibit 6 dB of
loss or more. Because of this, DCF's are usually co-located with EDFA's which also increase the overall cost
of the fiber. Since DCF has a small core size which
make it prone to certain types of nonlinearities. So DCF
also has high optical nonlinearities.
Fiber Bragg gratings were introduced in 1980 and have
been a subject of research with several applications. It is
device composed of an optical fiber that
contains a modulation of its core refractive index over a
certain length. The Grating reflects light propagation
through the fiber when its wavelength corresponds to the
modulation periodicity. The reflected wavelength (λ) is
called the Bragg wavelength, and is defined by the
Using fiber Bragg gratings for dispersion compensation
is a promising approach because they are passive optical
component fiber compatible, having low insertion losses
and costs. They are used as sensors, as wavelength
stabilizers for pump lasers, in narrow band WDM
add/drop filters and also as filters for dispersion
compensation. Advantages of FBG are that it helps in
minimizing the overall cost of the fiber and also it also
nΛ
is the effective refractive index of the grating in the fiber core and Λ is the grating
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
21
Methodology:
The structure of FBG varies via the refractive index, or
the grating period. The grating period can be uniform or
graded, and either localized or distributed in a
superstructure. The refractive index has two primary
characteristics, the refractive index profile which can be
uniform or apodized, and the offset which can be positive or zero.
On the basis of grating period, Fiber Bragg Grating can
be of four types –
1. Uniform gratings: In this gratings are done in fixed
interval shown in Fig 2(a).
2. Chirped gratings: In this gratings are done non-
uniformly as shown in Fig 2(b).3. Tilted gratings: In this gratings are done in uniform
manner but tilted as shown in Fig 2(c).
4. Superstructure: In this gratings are uniformly grouped
as shown in Fig 2(d).
Fig. 2: Types of Fiber Bragg Gratings
The most common advantage of FBG is low insertion
loss (IL). Typically, a 120-km FBG module has an
insertion loss in the range of 3 to 4 dB, depending on the type. The ability to tolerate high optical powers without
any loss caused by nonlinear effects is also one
prominent characteristic separating the FBG-DCM from
the DCF-DCM. A DCF displays non-linear effects at
low optical powers, but the FBG-DCM won’t introduce
such effects even at the highest power levels.
III. RESULTS AND DISCUSSIONS
In case of the FBG compensation, the minimum BER for
100 km SMF is -52.9 when it decreases to -27.45 for a
transmission path length of 200 km. If we go further
distance with the similar parameters, we get minimum
BER of -6 for 300 km SMF. This is shown in Fig. 3(b).
On the other hand, for DCF based system, we get minimum BER of -1000 for duration from 0.36 to 0.72
for 100 km SMF. If the length increases to 200 km, it
still remains the same for duration of 0.38 to 0.54. At
SMF length 300 km, we get minimum BER of -23
which is almost the same as the BER of 200 km SMF for
FBG compensation. This is shown in Fig. 3(a). All the
BER values are measured in log of BER. From this BER analysis, we can decide that the DCF technique can
provide better performance than the FBG technique in
long haul communication.
Another parameter for performance analysis is
the Q-factor. In the simulation three different values of
maximum Q-factor for three SMF lengths are being
measured. For FBG system, we got maximum Q-factor
15.12, 10.75 and 4.73 for 100, 200 and 300 km,
respectively. This is shown in Fig. 4(b). For DCF
system, maximum measured Q-factor is 177.21, 47.75
and 9.9 for 100, 200 and 300 km, respectively. So, the
Q-factor of 300 km using DCF is almost the same as the
Q-factor of 200 km for FBG technique. This is shown in
Fig. 4(a). Thus, we can say that using DCF technique
optical signal can travel more distance then that of FBG
compensation technique.
(a) DCF minimum BER 100 – 300 km
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
22
(b) FBG minimum BER 100 – 300 km
Fig. 3: Minimum BER of DCF and FBG compensation schemes for
different distances
(a) DCF maximum Q-factor 100 – 300 km
(b) FBG maximum Q-factor for 100 – 300 km
Fig. 4: Maximum Q-factor of DCF and FBG compensation
schemes for different distances
300 km
Fig. 3: Minimum BER of DCF and FBG compensation schemes for
300 km
300 km
factor of DCF and FBG compensation
schemes for different distances
(a) DCF for 100 km
(b) DCF for 200 km
(c) DCF for 300 km
Fig. 5: Eye diagrams for DCF compensation for 100, 200 and
300 km distances
DCF for 100 km
DCF for 200 km
DCF for 300 km
Fig. 5: Eye diagrams for DCF compensation for 100, 200 and
300 km distances
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
23
Fig. 6: Eye diagram for DCF scheme for 400 km distance
(a) FBG for 100 km
(b) FBG for 200 km
Fig. 6: Eye diagram for DCF scheme for 400 km distance
(c) FBG for 300 km
Fig. 7: Eye diagram for FBG compensation scheme for 100, 200 and
300 km distances
Finally, the Eye diagram for the two mentioned
techniques have been analyzed. Eye diagram is the
oscilloscope display of a digital signal. For the FBG
technique, we get acceptable eye shapes for up to
200km. The eye shape is not very good for a distance of
300km. This is shown in Fig. 7. On the other hand, for
DCF compensation a quite good eye shape for 300 km
SMF length is being observed. Though it
degraded to a poor shape after travelling 400 km. This is
shown in Fig. 5 and Fig. 6. Up to this poi
decided that, DCF technique is a better option than FBG
compensation for long distances (e.g.
After comparing all the performance parameters,
it can be asserted that DCF can perf
compensation even for a longer distance.
technique, minimum BER and Q-factor for 300 km
be achieved as those using FBG at 200 km. Even eye
height of DCF compensation for 300 km is better than
that of FBG at 200 km. So, it can beis a better technique than FBG for long
speed optical fiber communication.
IV. CONCLUSION AND FUTURE DIRECTIONS
Different dispersion compensation techniques for long
haul communication have been successfully studied.
After analyzing all the parameters of
different compensation techniques, it has been
concluded that the FBG technique is acceptable for
certain distance, but after travelling a longer distance, itsperformance degrades. We cannot make up that
degradation by altering any existing component of our
system. DCF can be a solution to overcome this
FBG for 300 km
Fig. 7: Eye diagram for FBG compensation scheme for 100, 200 and
300 km distances
Finally, the Eye diagram for the two mentioned
techniques have been analyzed. Eye diagram is the
oscilloscope display of a digital signal. For the FBG
technique, we get acceptable eye shapes for up to
200km. The eye shape is not very good for a distance of
300km. This is shown in Fig. 7. On the other hand, for
good eye shape for 300 km
. Though it has been
degraded to a poor shape after travelling 400 km. This is
6. Up to this point it can be
that, DCF technique is a better option than FBG
sation for long distances (e.g. 200km or above).
After comparing all the performance parameters,
that DCF can perform as well as FBG
r a longer distance. Using DCF
factor for 300 km can
FBG at 200 km. Even eye
300 km is better than
at of FBG at 200 km. So, it can be concluded that DCF chnique than FBG for long distance high
speed optical fiber communication.
AND FUTURE DIRECTIONS
Different dispersion compensation techniques for long
haul communication have been successfully studied.
After analyzing all the parameters of interest for
different compensation techniques, it has been
G technique is acceptable for a
certain distance, but after travelling a longer distance, its performance degrades. We cannot make up that
degradation by altering any existing component of our
system. DCF can be a solution to overcome this
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
24
difficulty. Even DCF is not sufficiently good after a
certain distance.
As it has been observed that DCF does not work well for
a distance like 400 km or more. Thus, a more suitable
method which can be used for longer distances like 500
km or more would be found in future. It has also been
planned to work on different DCF techniques and
compare them to find a better solution for long distance
optical fiber communication. Any two compensation
schemes can also be combined together for better results.
REFERENCES
[1] Shivinder Devra, Gurmeet Kaur, “Different
Compensation Techniques to Compensate Chromatic
Dispersion In Fiber Optics”.International Journal ofEngineering and Information Technology;volume-3,
issue-2: pp. 1-4;2011
[2] N. Ravi Teja, M. Aneesh Babu, “Different Types of
Dispersions in an Optical Fiber”.International Journal of
Scientific and Research Publications; volume-2, issue-
12: pp. 1-5;2012
[3] Md. J. Islam, Md. S. Islam, “Dispersion
Compensation in Fiber Communication Using Fiber
Bragg Grating”.Global Journal of researches in
engineering; volume-12, issue-2:pp. 21-25;2012[4] G. H. Patel, R. B. Patel, “Dispersion compensation in
WDM network using Dispersion compensating
Fiber”.Journal of Information, Knowledge and Research
in Electronics and Communication Engineering;
volume-2, issue-2: pp. 662-665;2013
[5] Pawan Kumar Dubey, Vibha Shukla, “Dispersion inOptical Fiber Communication”.International Journal of
Science and Research; volume-3, issue-10: pp. 236-
239;2014
[6] Gagandeep Singh, Jyoti Saxena, “Dispersion
Compensation Using FBG and DCF in 120 Gbps WDM
System”. International Journal of Engineering Science
and Innovative Technology; volume-3, issue-6: pp. 514-
519;2014
[7] Ashima Bhardwaj, Gaurav Soni, “Performance
analysis of 20Gbps Optical Transmission system using
Fiber Bragg Grating”.International Journal of Scientific
and Research Publications; volume-5, issue-1: pp. 1-
4;2015
[8] M Singh, “Different Dispersion CompensationTechniques in Fiber Optic Communication
System”.International Journal of Advanced Research in
Electronics and Communication Engineering; volume-4,
issue-8: pp. 2236-2240;2015
[9] R Singh., Prof. L Kumar, “Dispersion compensation
in Optical Fiber communication for 40 Gbps usingdispersion compensating Fiber”.International Journal for
Science and Emerging Technologies with Latest Trends;
volume-19, issue-1: pp. 19-22;2015
[10] A. J. Aggarwal, M. Kumar, “Comparison of
different techniques of Dispersion
compensation”.International Journal of Electronics and
Computer Science Engineering; volume-1, issue-2: pp.
912-918
[11] M. Singh, Rajbeer Rao, “Analysis of Dispersion
Compensation using Fiber Bragg Grating in Optical
Fiber Communication System”.International Journal of
Computer Applications; volume-126, issue-5: pp. 1-
5;2015
[12] Manpreet Kaur, H. Sarangal, “Dispersion
Compensation with Dispersion Compensating Fibers”.
International Journal of Advanced Research in
Computer and Communication Engineering; volume-4,
issue-2: pp. 354-356;2015
[13] Naveen Dalal, Dr. Amit Kumar Garg, “A
Comprehensive Study of Various Compensation
Techniques in High Speed Single Mode Optical Fiber
Communication”. International Journal of Recent Trends
in Engineering and Research; volume-2, issue-5: pp.
275-278;2016
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
25
An Improved Authentication Security Scheme
Nidhi Sharma
Ph.D Scholar
Dept of CSE, Baba Mast Nath
University,Rohtak
Dr. V.K Srivastava Professor
Dept of CSE, Baba Mast Nath
University,Rohtak
Alok Sharma Ph.D Scholar
Dept of CSE, Baba Mast Nath
University, Rohtak
Abstract—With the emergence of Big Data and Cloud
Computing, user authentication becomes more and more
difficult as Password security is a major issue for any
authenticating process. Password plays an important role in
various applications like internet services, net banking, ATM
machines etc. But passwords are not much safe to provide the
security to the users because of large no of attacks. Different
researches in past have proposed different techniques to make
the passwords most secured. In, this paper, a new technique is
proposed which involves three steps-salting, hashing, and then
generating a masked list. This masked list is stored in the
user’s password file. If any intruder attempts to log in using
any of these masked list words then an alarm is raised for the
application and the application can block that user or address.
Keywords— Big Data, Cloud computing, salting, hashing,
masked list, passwords security, honeywords.
1. INTRODUCTIONAs large amount of data is to be handled by cloud
computing so the foremost requirement in today’s digital world is information security which is normally secured by some authentication process.. There exist various methods for authentication (e.g. passwords, PINs etc) but the password based systems are the most generally used methods for authentication. But to store user passwords within databases as plaintext or only with their unsalted hash values is a blunder mistake. Many successful hacking attempts which enabled attackers to get unauthorized access to sensitive database entries including user passwords have been practiced in the past.[4]
Revealing of password files is a serious security problem that has affected many users and companies like Yahoo, LinkedIn, eHarmony and Adobe [2], since revealed passwords cause many possible cyber-attacks. These recent crisis has indicated that the weak password storage methods are presently in place on many web sites. For example, the passwords in the eHarmony system were stored using MD5 hashes without salt and also the LinkedIn passwords were also stored with unsalted hash values by using SHA-1 algorithm [3]. Even an attacker gets success to steal password file with the help of password cracking techniques it is easy to get most of the plaintext passwords.
According to this, there are two issues that should be acknowledged to control these security problems: First is passwords must be protected by taking relevant providence and storing with their hash values enumerated through salting mechanisms. And second is that a secure system should detect whether a password file is revealed or not to take relevant actions.
Lot of work is already defined by different researchers for password security. Earlier, Juels and Rivest used decoy passwords against hashed password databases to detect attacks. In there technique, the real password is stored with several honeywords for each user account in order to sense imitation. By using honeywords attacker cannot be sure if it is the real password or a honeyword even he has file of hashed passwords. And, if an attacker attempt to log in with a honeyword will trigger an alarm which notifying about password file breach to the administrator. Recently, Imran Erguler analyze the honeyword system and had suggested a different approach that selects the fake words from existing user passwords in the system to provide sensible honeywords and also to reduce storage cost of honeyword scheme. He give some remarks about the security of the system and pointed out that the key item for this method is the generation algorithm of the fake words such that they shall be unidentifiable from the correct passwords.
II.SALTING
A salt is random bits of characters used to modify a password before their hash value is calculated and it makes difficult to reverse the hashed password. Salt can be added to the hash to prevent a collision if another user in the system has selected the same password. Salt can be a combination of letters, numbers, characters or special characters etc. Salt will be added to make it more difficult for an adversary to crack password by using cracking methods because adding salt to a password hash prevents an attacker to check known dictionary words across the entire system [1]. The attacker has to produce every possible salt value. If the salt is longer and more complex, the greater time is required to crack the password. For every hash value different salt bits must be generated and for intruder a new dictionary must be generated for each stored password.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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III HASHING
Hashing is applied on the input password which is selected by the user. Hashing is a ‘one-way function’. This hash function takes input of any length, and after hashing produces a unique output of constant length. It is hard to decipher the hash and any attempt to crack it is practically infeasible. When storing passwords, the password is hashed by using hashing algorithm and then the resulting hash is stored in password file instead of plain text password.
IV.MASKING
It is basically the insertion of fake passwords associated with each user’s account. When an attacker gets the password list, he retrieves many password candidates for each account but cannot be sure about which password is real.[4] For each user account, the real password is stored with different mask words in order to sense impersonation. If different mask words are selected properly, an attacker who steals a file of hashed passwords cannot be sure whether the password is real or fake for any account. Here, Masking is a result of hashing which generates different permutations of real password. The main purpose of masking is to hide the real password with different fake passwords.
V.PHASES OF THE PROPOSED ALGORITHM
In the first step, add random strings of bits as a salt by applying salting techniques to password before their hash value is calculated and in second step after hashing, the crash list of real password is generated by using differential masking process. These two steps associated with this work are shown in figure1 :
Fig1: Proposed model
Phase I
Salting and Hashing Process
Password salting is adding a random string of characters to passwords before their hash is calculated to make
password hashing more secure and it makes them difficult to reverse as in Fig 2. The random string of characters can be a combination of letters, numbers and other special characters. This represents a salting process which generates salt bits for a specific binary string as shown in Table1.
The steps are as follows:
1. Get password
2. Generate salt using trusted random method
3. Append salt to original password
4. Generate salt hash password using hash function.
Fig2: Details of hashed passwords using the hashing and salting technique
Table1: Hashed Password after salting
From the result it is clear that password security using salting and hashing pattern provides a higher security because the user’s original ‘clear text’ password is not stored in database. Even if the password file is stolen by hacker/attacker or password file is public. Looking at the password file length of original password cannot be predicted which makes it difficult for hacker to break the password.
User
Password
Hashed Password after
salting
students123
india2020
mitchell
world2015
secrethash
worldwide45
3e7aca6e3d98efc1e1869e6c
2839216d410b4d43
f676a666362637b
8bd198c9398859d9
3b78e8e0a61afbc840a5
bef696a6d3c75404a871c1a
Step: I
Performing Salting and Hashing
(Add random salt bits to passwords before their
hash value is calculated)
Step: II
Differential Masking Process
(Crash List is generated)
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Phase II Masking Process
(To generate different fake passwords of real password)
For each user account, the real password is stored with different fake words in order to sense impersonation. Here, different permutations of real password are generated.
Suppose user selects a password ‘nidhi12’then this techniques creates a mask list of real password by masking process with different permutations of Q, I, R, X, Q-1 as shown in Fig 3
Fig3: To Generate different fake passwords of real
password
VI.CONCLUSION
This paper deals with user authenticity which is the most important aspect and introduce a new technique which converts the password provided into a series of hexadecimal strings (mask list) which are formed by applying hashing process using masking. One of these words is over hash string while the others form mask list. If any malicious user gets an access to the password file and attempts to login using any one of the words form the mask list then the system generates an alarm for the application concerned.
REFERENCES
[1] Search Security http://searchsecurity.techtarget.com/
definition /salt, Retrieved 15th Oct, 2011
[2] D. Mirante and C. Justin, “Understanding Password Database Compromises”, Department of Computer Science, Polytechnic Institute of NYU, Tech. Rep. TR-CSE-2013-02, 2013.
[3] K. Brown, “The Dangers of Weak Hashes, “SANS Institute Information Security Reading Room, Tech. Rep. 2013.
[4] Emin Islam Tatli, “Cracking More Password Hashes With Patterns”, Department of Electrical and Electronics Engineering, Istanbul Medipol University,2014.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Travel-Time Prediction: A Short Survey Vivek Agrawal Dr. J.N Singh Dr. Ashish Negi
Galgotia College of Engg and Tech Galgotia University UTU
Greater Noida Greater Noida Dehradun
[email protected] [email protected] [email protected]
Abstract - Travel-time information could be applied
in various fields and its purpose is to improve the
reliability and service quality. From the traveler’s point
of view, the travel-time information helps to save the
time and improve reliability through the selection of
travel routes pre-trip and en-route. One way to
accomplish this is to provide driver or passenger with
current traffic information throughout their trip. In the
application of logistics, travel-time information could
reduce the delivery costs, increase the reliability of
delivery, and improve the service quality. For traffic
managers, this information is an important aspect for
the smooth operation of traffic system.
Keywords- Intelligent Transport Systems, Travel
time prediction, Micro-simulation model.
1. INTRODUCTION
Travel time has been identified as an important performance measuring aspect and regular surveys are now being conducted in the capital cities by state road authorities. Meanwhile, a large number of research studies and literature reviews concerned with the field of travel-time prediction have demonstrated the importance of travel time information in practical applications of transportation and logistics. Some of the methods strive to measure travel time directly using vehicle re-identification technology ([2, 6, 1]). These methods require video cameras or other special purpose equipments. In [1], Coifman proposed a methodology which can match a portion of vehicles using vehicle length measurements, but the method still requires double-loop speed measurements.
Traffic data is divided in following three categories: historical, current, and predictive [16]. Travel-time prediction is usually distinguished into two main approaches: statistical models and analytical models. Statistical models is characterized as data driven methods that generally use a time series of historical and current traffic variables such as travel times, speeds, and volumes as input. Analytical models predict travel times by using microscopic or macroscopic traffic simulators, such as METANET [18], [19], NETCELL [21], and MITSIM [20].
Given the historical travel-time data f(t-1), f(t-2), ……f(t-n) and at time t-1, t-2, ……t-n respectively, we can predict the future values of f(t+1), f(t+2),…..by analyzing the historical data set. On the basis of the relation between the time-variant historical data set and its results, the prediction of future values can be done. Numerous statistical methods on the accurate prediction of
travel time have been proposed, such as the ARIMA model [17], linear model [15], and neural networks [7].
The purpose of this paper is to review the applications and the prediction methodologies of travel-time from previous research and highlight some of the results from survey data that can be used in the further research. The goal of the research is trying to develop and test a potential research methodology to promote the efficiency and accuracy of travel-time prediction for its further applications and development in arterial roads.
This paper is organized as follows. Section 2 covers related work. Section 3 defines the methodology developed. Section 4 describes the experiments. Finally, Section 5 contains the conclusions.
II.RELATED WORK
The concept of Trajectory Pattern introduced in [11] defines a sequence of geo-referenced objects Sof size m and a list of Temporal Annotations A ofsize (m - 1), whose values represent the temporaldistance between two consecutive geo-referencedobjects belonging to S. The information of thetrajectory patterns, i.e., the geo-referenced objectsand temporal annotations, are extracted from a setof trajectories of moving objects (in this case, asequence of triple <latitude, longitude, timestamp>)by identifying regions of interest (geo-referencedobjects) often visited by moving objects. Thecomputation of temporal annotations is formalizedas a problem of density estimate, whose values areused to calculate the time difference betweentimestamps of two consecutive triples in atrajectory.
Liao et al. [12] shows that a person after certain time follows a routine and the same routine can be "learned" by the existing system. For the description of the routine, the author take care of three characteristics: location, change of transportation mode and main locations. On the basis of above characteristics, the authors can develop a mechanism to predict the location, the changes in the transportation mode, and the prime locations that he or she will pass.
Monreale et al. [3] authors created a mechanism called WhereNext to estimate the next possible locations of a moving object with more and more accuracy. The mechanism uses the previously extracted movement patterns, which represent possible behaviors of moving objects, like pattern of regions generally visited by object in
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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motion. A decision tree, namely a T-Tree pattern, is constructed according to trajectory patterns (as defined in [1]) that were already extracted and evaluated
IdŽ et al. [4] works on the method of predicting travel time for an arbitrary pair of source-destination on a map. The proposed method works in the direction of probabilistic prediction of the travel time along an unknown path (a sequence of links), and the path similarity is defined as the kernel function. Their work introduces two novel ideas. The first one is the use of a kernel string to represent the similarity between paths. The second one is the application of the Gaussian process regression for predicting the travel time.
Wu et al. [5] have similar goals, but their approach is different. The authors use Support Vector Regression (SVR) for the estimation of the time that a particular part of highway will be covered, based on the collection of data by speed gauges posted along specific roadways in Taiwan. Also according to the authors, SVR had better results than Artificial Neural Networks because SVR is more amenable to generalization than Artificial Neural Networks.
The works [9] and [10] of Alvarez et al. use the definition of the conceptual model of trajectories with segmentation by stops and moves proposed in the work cited above [8]. The first makes the discovery of movement patterns for trajectories based on data mining techniques. It proposed a framework to model and performs the mining for discovery of movement semantic patterns. The main focus is on discovering the most frequent moves between two stops, where each stop is seen as an application's interest.
Still based on the conceptual model of Spaccapietra et al. [8], the works of Palma et al. [11] and Rocha et al. [12] followed by clusteringapproaches to knowledge discovery. The firstproposes a solution to the discovery of importantplaces in the trajectory based on speed, i. e., and thestops discovery. The work is based on the simpleidea that segments with lower speed may representlocal interest in two parts: the first part of theprocess operates in the discovery of potential stopsand the second based on the outcome of the first,analyzes them related the geographical information.The second seeks to discover places of interestbased on changes of direction, considering areaswhere this aspect is important as the discovery ofplaces where the ships perform sea fishing,preventing them engaging activity in forbiddenplaces.
On another line of work, Guc et al. [13] supports the idea that the trajectory data can be used to facilitate the manual process of trajectories semantic annotation. For this, they propose a trajectory annotation model based on notion of episodes that allows the separation between the physical and semantic part and also architecture to program to perform semantic annotations. Yan et al. [14] work stays on the same line and proposes a
framework (SeMiTri) of general propose to various domain applications (i.e. to heterogeneous trajectories) that lets you manage and enrich trajectories with semantic annotations, allowing the application can benefit from a semantic representation of movement through the inferences made from space-time properties of the position raw data (e.g. extraction of stops and moves, tracking its direction or movement pattern), geographical regions covered by the trajectories (e.g. streets and notables local) and application objects related to the trajectories (e.g. customers, parking).
The framework takes advantage of the proposed model for semantic trajectories, where a trajectory is represented as a sequence of semantic episodes that correspond to a interpretation of the application and also presents three algorithms for performing trajectory annotations, one based on regions of interest, another based on the path and the latter based on points of interest, these algorithms are responsible for covering the peculiarities of the heterogeneity of trajectories, as trajectories of vehicles, people walking, animals, etc.
III.METHODOLOGY
The methodology for estimating the time that will be spent by a vehicle between any two points is focused on: (1) pre-processes raw trajectory data to create aggregated data and (2) To learn the behavior of the vehicles using this aggregated data.
A. Moves and stops
Any moving object moves and stops. The stops have a goal (semantic) and a period of time: to take a bus, a person waits for 10 minutes at a bus stop or a vehicle stops for 5 minutes to refuel at a gas station. Figure 1 shows a situation in which a moving object stops at P1, P2 and P3 along the path between A and B in the space S.
Taking into account the moves and stops of moving objects, the travel time prediction mechanism uses data mobility (GPS coordinates with timestamp and speed) to "learn" the behavior of vehicles (and their drivers) in a particular region and calculate the time a vehicle (driven by the same driver) will take to move between two distinct points.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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B.Mobility data extraction
The travel time estimation mechanism is characterized by the historical movements of vehicles over time, as the goal is to estimate the travel time of a vehicle, associated with a driver, between two specific points. Thus, it is necessary to know when and where the vehicle has moved and when and where it has stopped. This is done by pre-processing the data to create more qualitative information indicating moves and stops (figure 2) when compared to simply positioning the vehicles in time (figure 3). A stop is detected when the vehicle does not travels s meters in t minutes, where s and t are parameters of the stop detection function and the stop location is a known place based on the application context
Figure 2 - Extraction of data movement
through the positioning data.
The model in figure 4 illustrates the relationship between move, stop, vehicle, driver and region. Whereas the data are divided by regions, every movement and stop of a vehicle are associated with a region and a driver.
C.Trafficked segment processing
On the path between two stops, the speed of vehicle varies, resulting in some parts to be slower than others. The segmentation helps in finding the time vehicle will take from the current position to the next known (again, based on the application context) location it will stop with segment slices of different average speed. The segmentation is done in sections, as shown in figure 3. Considering the moves of a vehicle between the stops P1 and P2, four segments are defined: S1, S2, S3 and S4. Segment S1 starts at P1 and ends at P2. Segment S2 starts at the position indicated in the figure and ends at stop P2. Segment S3 starts at the position indicated in the figure and ends at stop P2. And finally, segment S4 starts at the position indicated in the figure and ends at stop P2. Another way to segment the movement would be as follows: the first segment would be from P1 to S2. The second segment would be from S2 to S3. The third segment would be from S3 to S4. And finally the last segment would be from S4 to P2. But the first targeting option was chosen
Figure 3 - Illustration of movement
segmentation.
The segmentation of movements creates a new entity in the model illustrated in figure 4: the Segment entity. Thus, a movement (Move) has a set of entities Segment. The entity Segment has as attribute the start point (or coordinate), the end point (or coordinate), the start timestamp, the end timestamp and the distance traveled.
Figure 4 - relationship between move, stop, vehicle,
driver and region
IV.MODELING APPROACHES:
Simulation method The simulation method uses a traffic model to
calculate the routing of vehicles from first principles based on established modeling algorithms. It is potentially a very powerful tool, provided that the underlying model is robust and there is sufficient data to allow accurate calibration.
Statistical method The statistical method is based on mathematical
relationships developed through statistical regression analysis. It is the simpler of the two methodologies which means it should be quicker and cheaper to implement, but this also limits its capabilities.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Table1: Analysis of Approaches
Features Simulation-based approach Statistics-based approach
Basis of system Model calculating vehicle
routes from first principle.
Mathematical relationships between road segment and
time
Network
size/complexity Can be highly complex, eg.
Urban road network.
Can include route choice.
Simple networks (eg single
corridor network). Limited
route choice.
Data requirements Complex: Additionally
requires data on travel
behavior
Basic: ‘Live feed’ of traffic
flows, speeds and external
events, plus historic log.
Ability to predict
during events
Good. Plus, can be used to
pre-empt planned events
Good if event has occurred
before, otherwise limited.
Cost to implement High. Modest.
Cost to monitor and maintain
High. Will generally need
constant monitoring and
require periodic recalibration.
Low. Will be largely self-
maintaining and will
require low levels of monitoring.
Software
available? Yes. No.
V.PREDICTION METHODOLOGY AND
ERROR MEASUREMENTS
Suppose that the current time is t and we want to
predict y(t+l) at the future time t+l with the
knowledge of the value y(t-n), y(t-n+1), ……..y(t)
for past time t-n, t-n+1, ……t. respectively. The
prediction function is expressed as
y(t+l) = f(t, l, y(t), y(t-l), …….y(t-n))
We examine the travel times of different
prediction methods. Relative mean errors (RME)
and root-mean-squared errors (RMSE) are applied
as performance indices
RME = ∑=
−n
i Yi
YiYi
n 1
*1
RMSE = ∑=
−n
i Yi
YiYi
n 1
2*1
Where Yi is the observed value and Yi* is the
predicted value.
1. Travel-Time Predicting Methods
To evaluate the applicability of travel-time prediction, some common travel-time prediction methods are exploited for performance comparison
A. Current Travel-Time Prediction Method
This method computes travel time from the data available at the instant when prediction is performed [14]. The travel time is defined by
T(t,∆) = ∑−
=
+
∆−
−1
0
1
),(
L
i i
ii
txv
xx
Where ∆ is the data delay, L is the number of
sections, (xi+1-x) denotes the distance of a section
of a highway, and v (xi, t-∆) is the speed at the start
of the highway section.
B. Historical Mean Prediction Method
This is the travel time obtained from the average
travel time of the historical traffic data at the same
time of day and day of week.
T(t) = ∑=
w
i
tiTw 1
),(1
Where w is the number of weeks trained and is the
past travel time at time of historical week.
Table: Prediction results in RME and RMSE of different predictors for traveling different distances (all
testing data points)
RME Current-time
predictor
Historical-mean
predictor
SRV-Predictor
45 km 9.29% 12.52% 3.91%
161 km 3.88% 5.01% 1.71%
350 km 2.85% 2.56% 0.96%
RMSE Current-time
predictor
Historical-mean
predictor
SRV-Predictor
45 km 28.75 16.20% 6.79%
161 km 9.98% 6.66% 2.57%
350 km 5.49% 3.42% 1.33%
The results in Table II show the RME and RMSE of different predictors for different travel distances over all the data points of the testing set. They show that the SVR predictor reduces both RME and RMSE to less than half of those achieved by the current time and historical-mean predictors for all different distances.
All three of the predictors predict well for long distance (350 km), but this makes it difficult to compare the performances of the three predictors.
VI.FURTHER RESEARCH
This paper provides a review of travel-time studies that includes variables of travel time, measurement of travel time, methodologies of travel-time prediction and estimation, research difficulties. The application of micro-simulation techniques could help to overcome the current difficulties of travel-time studies. In addition, the link up with the SCATS traffic control system might extend travel-time prediction from isolated
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
32
environment to arterial road sections by minimizing the uncertainty in factors from signal systems.
Adapting micro-simulation techniques to be a test-bed can solve the problem of data shortage and also perform the various strategies on the test-bed. The method could contribute to the efficiency of related research, as long as a well validated simulation model is available. The final stage of the research will still involve the use of real traffic data from field survey to validate and adjust the developed travel time prediction model.
There are a many areas where further research could be done, in an attempt to improve the model further:
• As discussed in earlier notes, there are anumber of issues with the underlying data, namely that there are large gaps in the data for some of the links. Sourcing of improved or alternative data sources, or improved interpolation may result in more accurate predictions.
There should also be consideration of additional explanatory variables, such as the weather, which could not be considered in this study due to data issues.
VI.CONCLUSION
Predicted travel-time information provides the capacity for road users to organize travel schedule pre-trip and en-trip. It helps to save transport operation cost and reduce environmental impacts. Besides, accurate travel time information also helps delivery industries to promote their service quality by delivering on time. However the development of travel time estimation and prediction are suffered from the shortage of traffic data sets and too much interference from transport environment.
A comprehensive literature review was undertaken which examined a wide range of previous studies and research. The outcome of this review indicated that of the two potential solutions the statistical approach was the most suitable, based on the availability of the data and the client’s requirements. The travel time estimation for moving vehicles involves a large number of variables, which makes the solution complex. Changes in traffic behavior greatly influence the travel estimated time of the vehicle. But the vehicle driver itself, the vehicle’s features, the vehicle load, if the day is before a holiday, are also variables to be considered, in addition to the vehicle identifier.
REFERENCES
[1] B. Coifman. “Vehicle reidentifcation and traveltime measurement in real-time on freeways using the existing loop detector infrastructure”. Transportation Research Record, (1643):181191, 1998.
[2] B. Coifman, D. Beymer, P. MeLaughlan, and J.Malik. “A real-time computer vision for vehicle tracking
and traffic surveillance. Trasnportation Research”: Part C, 6(4):271288, 1998.
[3] Monreale, A. et al. “WhereNext: a location predictor on trajectory pattern mining”. KDD 2009, p. 637–646, 2009.
[4] IdŽ, T. and Kato, S. “Travel-Time prediction using Gaussian process regression: a trajectory-based approach”. SIAM Intl. Conf. Data Mining (2009)
[5] Wu, C-H., Wei, C-C., Ming-Hua Chang, M-H.,Su, D-C. and Ho, J-M. “Travel Time Prediction with Support Vector Regression”. Proc. Of IEEE Intelligent Transportation Conference. October, 2003 pg. 1438-1442.
[6] R. Kuhne and S. Immes. “Freeway control systems for using section-related traffic variable detection”. In Pacific Rim TransTech Conference Proceedings, volume 1, pages 5662. ASCE, 1993.
[7] J.W. C. van Lint, S. P. Hoogendoorn, and H. J.van Zuylen, “Robust and adaptive travel time prediction with neural networks,” presented at the Proc. 6th Annual Transport, Infrastructure and LogisticsCongr., Delft, The Netherlands, Dec. 2000.
[8] Spaccapietra, S., Parent, C., Damiani, M. L., deMac•do, J. A., Porto, F., and Vangenot, C. “A conceptual view on trajectories”. Data & Knowledge Engineering 65, 1 (2008), 126-146.
[9] Alvares, L. O., Bogorny, V., de Mac•do, J. A. F.,Moelans, B., and Spaccapietra, S. “Dynamic modeling of trajectory patterns using data mining and reverse engineering” at the Tutorials, posters, panels and industrial contributions at the 26th International Conference on Conceptual Modeling - ER (Auckland, New Zealand, 2007), A. H. F. L. L. M. John Grundy, Sven Hartmann and J. F. Roddick, Eds., vol. 83 of CRPIT, ACS, pp. 149-154.
[10] Alvares, L. O., Bogorny, V., Kuijpers, B., deMac•do, J. A. F., Moelans, B., and vaisman, A. A. “A model for enriching trajectories with semantic geographical information”. In Proceedings of the 15th ACM International Symposium on Geographic Information Systems (New York, NY, USA, 2007), GIS '07, ACM, pp. 22:1-22:8.
[11] Giannoti, F., Nanni, M., Pinelli, F. andPedreschi, D. “Trajectory pattern mining”. KDD 2007, p. 330-339.
[12] Liao, L., Patterson, D., Fox, F. and Kautz, H.“Learning and inferring transportation routines”. Artificial Intelligence, v.171 n.5-6, p.311-331, April, 2007.
[13] S. Ruping. mySVM Software [Online]http://www-ai.cs.uni-dort- mund.de/SOFTWARE /MYSVM/
[14] D. Park and L. R. Ritett, “Forecasting multiple-period freeway link travel times using modular neural networks,” presented at the 77th Annu. Meeting Transportation Research Board, Washington, DC, Jan. 1998.
[15] X. Zhang, J. Rice, and P. Bickel, “Empirical comparison of travel time estimation methods, Tech. Rep. Dept”. Stat., Univ. California, Berkeley UCB-ITS-PRR-99-43, Dec. 1999.
[16] R. Chrobok, O. Kaumann, J.Wahle, and M.Schreckenberg, “Three categories of traffic data: Historical, current, and predictive,” in Proc. 9th Int. Fed. Automatic Control Symp. Control in Transportation Systems, 2000, pp. 250–255.
[17] E. Fraschini and K. Ashausen, “Day on Day Dependencies in Travel Time: First Result Using ARIMA Modeling: ETH”, IVT Institute for Verkehrsplanung, Transporttechnik, Strassen-und Eisenbahnbau, Feb. 2001.
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[18] Q. Yang and H. N. Koutsopoulos, “Amicroscopic traffic simulator for evaluation of dynamic traffic management systems,” Transport. Res., pt. C, vol. 4, no. 3, pp. 113–129, 1996.
[19] A. Messmer and M. Papageorgiou, “METANET: A macroscopic simu-lation program for motorway networks,” Traffic Eng. Contr., vol. 31, no. 549, pp. 466–470, 1990
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s
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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A Survey of Traffic Management and Behaviour
in A QOS Environment.
Tarun Gupta Department of Electronics and Communication Engineering, Research Scholar, DCRUST University, Murthal,
Haryana, India [email protected]
Amit Kumar Garg Department of Electronics and Communication Engineering, Professor in ECE Dept., DCRUST University,
Murthal, Haryana, India [email protected]
Abstract— In this paper an efficient Quality of Service (QoS) oriented traffic
management schemes has been proposed which is based on several types of
packet handling techniques. Queuing is one of the vital mechanism in a traffic
management system. Each router in the network must implement some queuing
discipline that governs how packets are buffered while waiting to be transmitted
Simulation results obtained that proposed Weighted Fair Queuing (WFQ)
technique is better than conventional techniques such as First in First Out (FIFO),
Priority Queuing (PQ) in terms of Packet dropped, Packet end to end delay for
various network services like Video Conferencing for bursty traffic. This paper
emphasizes the average queuing delay for Poisson and Self-similar traffic models
with two algorithmic approaches: Greedy algorithm and Evolutionary algorithm.
The greedy algorithm performs a least cost search on the total delay along paths
for routing traffic in a multi-hop fashion, the evolutionary algorithm uses the
genetic methods to optimize the average delay in a network. The results showed
that virtual topology design for self-similar traffic is quite different from the virtual
topology design for poisson traffic model using the standard mentioned
algorithms.
Keywords—IP Quality of Service; Latency; Greedy algorithm;
Wavelength Division Multiplexing.
1. INTRODUCTION
In a growing trend traffic is increased rapidly so in order to
make our network efficient in such conditions, traffic is processed as quickly as possible but there is no guarantee of
timelines or actual delivery. With the rapid transformation of the Internet into a commercial infrastructure, demands for
service quality have rapidly developed. Many challenges came on to the picture how to provide Quality of Service (QoS) for
applications such as Internet telephony and video-conferencing which requires a higher QoS than electronic mail
and general web browsing. In an IP network, many methods has been proposed to implement QoS such as fair queuing,
weighted fair queuing, frame-based fair queuing etc. However, all of these methods are based on employing buffers at the
network nodes. To implement the existing QoS mechanisms to differentiate services, all intermediate nodes should have a certain amount of buffer space. However, the use of electronic
buffer necessitates optical to electrical (O/E) and electrical to optical (E/O) conversions which sacrifice the data
transparency. On the other hand, no optical buffer (RAM) is available and the use of fiber-delay lines (FDLs) which can provide a limited delay should also be avoided as much as possible in the optical layer.
The measure problem in a network are related to the allocation of network resources as buffers and link bandwidth to different users. A limited amount of resources has to be
shared among many different competing traffic flows in an efficient way in order to maximize the performance and the
use of the network resources. The behavior of routers in terms of packet handling can be controlled to achieve different kind
of services [5]. This proposed paper indicates the performance of a number of packet handling mechanisms and produces a
comparative picture of them using the simulation software OPNET Modeler (version 14.5) [14]. The Opnet modeler is
one of the most advanced tools from among Opnet products palette, together with additional modules such as Wireless for
defence, 3D network visualize (3DNV), Application Characterization Environment (ACE) and system in the loop
(SITL) modules which allows advanced simulation methods for wired and wireless communication networks.
In this paper, the problem of designing virtual topologies for multi-hop optical WDM networks when the traffic is self-similar in nature is considered. Studies over the last few years
suggest that the network traffic is bursty and can be much better modeled using self similar process instead of Poisson
process. Here examine the buffer size of a network and observe that even with reasonably low buffer overflow
probability, the maximum buffer size requirement for self-similar traffic can be very large. Therefore, a self-similar
traffic model has an impact on the queuing delay which is usually much higher than that obtained with the Poisson
model.
1. State of the art in QoS issues for packet handling
Techniques.
Various queuing disciplines can be used to control which
packets get transmitted and which packets get dropped. The proposed scheme comprises of (i) First-in-first-out (FIFO)
queuing. (ii) Priority queuing (PQ) (iii) Weighted-Fair queuing (WFQ).
In the first case, this technique describes the principle of a queue or first-come first serve behavior: what comes in first is handled first, what comes in next waits until the first is
finished etc. Thus it is analogous to the behavior of persons “standing in a line” or “Queue” where the persons leave the
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
35
queue in the order they arrive. First In First Out (FIFO) is the most basic queuing discipline. In FIFO queuing all packets are
treated equally by placing them into a single queue, then servicing them in the same order they were placed in the
queue. FIFO queuing is also referred to as First Come First Serve (FCFS) queuing [7]. Priority Queuing assigns multiple
queues to a network interface with each queue being given a priority level. A queue with higher priority is processed earlier
than a queue with lower priority. Priority Queuing has four preconfigured queues, high medium, normal and low priority
queue. By default each of these queues has 20, 40, 60 and 80 packets capacity [4-5]. If packets arrive in the high queue then
priority queuing drops everything its doing in order to transmit those packets, and the packets in other queue is again empty.
When a packet is sent out an interface, the priority queues on that interface are scanned for packets in descending order for priority. The high priority queue is scanned first, then the
medium priority queue and then so on. The packet at the head of the highest queue is chosen for transmission. This
procedure is repeated every time when a packet is to be sent. The maximum length of a queue is defined by the length limit. When a queue is longer the limit packets are dropped [5].
In QoS, a flow-based queuing algorithm that schedules
low-volume traffic first, while allowing high-volume traffic share the remaining bandwidth. This is handled by assigning a
weight to each flow, where lower weights are the first to be serviced [5]. WFQ is a generalization of fair queuing (FQ). Both in WFQ and FQ, each data flow has a separate FIFO queue. In FQ, with a link data rate of R, at any given time the
N active data flows (the ones with non-empty queues) are serviced simultaneously, each at an average data rate of R / N.
Since each data flow has its own queue, an ill-behaved flow (who has sent larger packets or more packets per second than the others since it became active) will only punish itself and not other sessions [1-4]. In the mentioned techniques, the
study has been carried out on some issues like traffic dropped,
packet end to end delay and simulation results indicates that WFQ technique has a better quality than conventional
techniques in all the mentioned issues.
2. Proposed efficient QoS scheme
The proposed scheme comprises of (i) Greedy algorithm
(GA) (ii) Evolutionary algorithm (EA). In the proposed scheme, two different algorithm approaches is used for
solving the problem by considering both the queuing delay and the propagation delay of a network while designing a virtual topology. Researchers of optical networks have
attempted such problems of designing virtual topologies and have obtained solutions using heuristics/methods in
polynomial time [1-4]. Mukherjee et al. in [10] formulated the virtual topology design problem as a nonlinear optimization
problem where the objective was minimization of average network delay. In [1], Banerjee and Mukherjee formulated the
virtual topology design problem as a linear program where the queuing delays were intentionally ignored in the formulation.
This is of the opinion that the queuing delays are negligible with respect to the propagation delays when the load per
channel is reasonably low and cited the results obtained in [10] as reason to neglect the queuing delay.
Greedy algorithm greedily finds the connection with least delay from an initial topology constructed using first-fit
wavelength assignment policy. In first fit wavelength assignment strategy all the wavelengths are numbered and the
wavelength with lowest number or subscript has been given highest priority for wavelength assignment. In this strategy, a free wavelength with lowest subscript is assigned on all the
links along a route to establish the connection request. If the lowest subscript wavelength is not free then the connection
request is tried on second available free wavelength if it is also not free then the third wavelength is tried and so on. Thus for every connection request a free wavelength is searched according to lowest index from the available set of
wavelengths. This strategy does not check the path length of a connection request.
Evolutionary algorithms are emerging as a good alternative for solving hard optimization problems, we next propose an Evolutionary algorithm that tries to optimize the average delay of a network. The algorithm uses a hybrid routing and
wavelength assignment policy for the initial topology and then acts upon it in the evolutionary way to construct the final
topology. In the proposed wavelength assignment strategy a wavelength is assigned to the connection request according to the path length. The connection requests with short path length are given more wavelengths as compared to the connection
requests with long path length. This will lead to improved
system performance in terms of blocking probability. In this strategy, two sets of wavelengths are defined viz. set 1 and set
2. In set 1 all the wavelengths are available for connectionrequests and in set 2 few higher indexed wavelengths are
available only. For example if total available wavelengths areassumed 5 in the network then
Set 1= (λn), where n= 1 : 5
Set 2= (λm), where m= 4 : 5
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
36
The connection requests with short path length will be served by set1 and the connection requests with long path
length will be served by set 2. Here the connection requests are categorized into two categories viz. short path length and
long path length category according to path length. The connection requests with path length less than or equal to X
(where X is the opted path length for a network topology) are treated as short path length category and the connection
requests with path length greater than X are treated as long path length category. To determine the value of X, offline
calculation of all possible s–d pairs and their path length has been done.
Average queuing delays in the network with the Greedy, Evolutionary, and the Heuristic [10] algorithms for Poisson
and Self-similar traffic models are shown in Figs. 3 and 4 respectively. It has been observed that while the average queuing delay of the network considering Poisson model is
less than 2 milliseconds in most cases (Fig.3), it can be almost five times higher when the traffic is self-similar in nature
(Fig.4 ). The heuristic algorithm of [10] shows much higher queuing delays using the self-similar traffic model compared to the Poisson model as it constructs the virtual topology independent of the network delay. From the figures it can be
seen that the results improve in the cases of Greedy and Evolutionary algorithms, because these algorithms optimize
the topology considering both propagation and queuing delays [5]. The virtual topology constructed with the Greedy algorithm shows high average queuing delays for both Poisson and self-similar models when the number of wavelengths is 4,
but it improves as the number of wavelengths increases [11]. This is because, the virtual topology initially constructed with
the first fit wavelength assignment policy which does not necessarily optimize delay in the network. As the number of wavelengths increases, more single hop connections are established thereby reducing the delay. It is also observed that
when estimated with the standard M/M/1 queuing model all
the three algorithms give low (almost negligible) and similar queuing delays (Fig.3), whereas, it is not the case with self-
similar traffic model (Fig.4 ). This shows that although algorithm is independent of queuing delays (or neglecting
queuing delays) can be used effectively for designing a good virtual topology with Poisson traffic model, for bursty or self-
similar traffic it is necessary that the algorithms consider queuing delays of the network for the best network performance. For an efficient network performance the average queuing delay is least in any optical network.
3. Simulation results
Fig. 1 show the results of traffic drop versus
transmission rate respectively. The performance measure is estimated by varying an transmission rate from interval to interval. As shown in Fig. 1, it is seen that the packet drop starts at around 95 sec. The simulation results obtained that
packet drop for FIFO is higher, for PQ it is semi lower and for WFQ it is lower.
Fig.1. Traffic drop vs. transmission rate
Fig. 2 shows packet end to end delay versus transmission rate for video conferencing services [12-13], where it can be
observed that as the traffic increases the packet end to end delay time is smaller for WFQ group then PQ and FIFO groups. For an efficient network performance, packet end to
end delay is least. This parameter was tested for Video Conferencing as it requires higher QoS in comparison of other
type of traffic. Like Video conferencing, in VOIP services also FIFO and PQ groups packet end to end delay time is always
higher than WFQ.
Fig.2. End to End delay vs. transmission rate
Fig.3 and Fig.4 show the results of comparison of the average queuing delay for Poisson model [6] and self-similar traffic model [8] with the greedy and evolutionary algorithm as well as the conventional heuristic algorithm. Fig. 3 shows
that average queuing delay is too small for Poisson model with different algorithm approaches. So for such type of models
if algorithm is made independent of queuing delays (or neglecting queuing delays) it can be used effectively for designing a good virtual topology [9].
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
37
Fig.3. Average queuing delay vs. number of wavelengths for
(Poisson model)
Where Gss and Gp: Results with the Greedy algorithm
using the self-similar model and the Poisson model respectively. Ess and Ep: Results with the Evolutionary algorithm using the self-similar model and the Poisson model respectively. Hss and Hp: Results with the Heuristic algorithm
of [10] using the self-similar model and the Poisson model respectively.
Fig.4 obtain the results that average queuing delay is comparatively more in self-similar model with different algorithm approaches. So for the proposed bursty or self-similar traffic it is necessary that the algorithms considering
the queuing delays while designing a good network virtual topology.
Fig.4. Average queuing delay vs. number of wavelengths
for (Self-similar model)
III.CONCLUSION
In this paper, an efficient Quality of Service (QoS) oriented traffic management schemes has been proposed to
reduce the average queuing delays by designing a different virtual topology for the networks. It is seen that the proposed
scheme guarantees that WFQ shows the better performance
among all the conventional queuing techniques in terms of Packet drop, File receiving, voice data receive and video
conferencing. However there are some problems with the integration occurs which can delay the voice data in reaching
the proper address at a continuous rate. So in order to solve the time delay problem the results of WPQ algorithm can be
applied. For voice communications over IP to become acceptable to the users, the delay needs to be less than a
threshold value and the IETF (Internet Engineering Task Force) are working on this aspect . Delay for applications like
video conferencing are very apparent, causing the video signal to jerk and sputter. Fair Queuing algorithm can solve the
problem. But according to the simulation it is already proved that a modernized format of fair queuing WFQ (Weighted Fair
Queue) can perform better. So, it can be said with confidence that user traffic stream like voice, video, data can be easily transferred with its efficient level performance by using
Weighted Fair Queue algorithm. The obtained results shown that virtual topologies designed considering self-similar traffic
is quite different from the virtual topologies designed using the standard Poisson model as former model design is dependent on the queuing delays which cannot neglect and therefore more effective in handling the present day bursty
Internet traffic.
REFERENCES
[1] B. Mukherjee, D. Banerjee, S. Ramamurthy, A.Mukherjee, Some principles for designing a wide-area WDM optical network, IEEE/ACM Transactions on Networking, vol. 4, no. 5, (Oct. 1996), pp. 684–695.
[2] D. Banerjee, B. Mukherjee, Wavelength routed optical networks: Linear formulation, resource budget tradeoffs and a reconfiguration study, IEEE/ACM Transactions on Networking, vol. 8, no. 9, (Oct. 2000), pp. 598–607
[3] J. A. Bannister, L.Fratta, M.Gerla, Topological design of the wavelength-division optical network, Proc. INFOCOM 90 (San Francisco, LA, USA, June 1990), pp. 1005–1013.
[4] Maheshwari, Harish, Mandhania, Sonali Sisodia “VoIP Technology: Overview and Enhancements” (MCA, I.I.P.S , D.A.V.V).
[5]OpnetModeler,OPNET14.5<.http://www.optnet.com/optnetmodeler[online[6] P. Fiorini, L. Lipsky, H.-P. Schwefel, Analytical models of performance in telecommunication systems based on On-Off traffic sources with self-similar behavior, Proc, of 7th Int. Conf. Telecommunication Systems and Modeling (Nashville, TN, USA, Mar, 1999).
[7] R.M. Krishna Swamy, K.N. Sivarajan, Design of logical topologies: A
linear formulation for wavelength routed optical networks with no wavelength changers, IEEE/ACM Transactions on Networking, vol. 9, no. 2, (Apr. 2001), pp. 186–198.
[8] R.Ramaswami, K. N. Sivarajan, Design of logical topologies for wavelength-routed optical networks, IEEE Journal of Selected Areas of Communications, vol. 14, no. 5, (June 1996), pp. 840–851
[9] S. Banerjee, B. Mukherjee, D. Sarkar, Heuristic algorithms for constructing optimized structures of linear multihop lightwave networks, IEEE Transactions on Networking, vol. 2, no. 2–4, (Feb.–Apr. 1994), pp. 1811–1826.
[10] Setrag Khoshafian, A. Brad Baker; “Contributor A. Brad Baker”, vol. 2, no. 4, (July 2006), pp. 122-132
[11] T.G.Robertazzi, Computer Networks and Systems: Queuing Theory and Performance Evaluation (Springer-Verlag), 2000.
[12] V.Paxson, S. Floyd, Wide-area traffic: The failure of Poisson modeling, IEEE/ACM Transactions on Networking, vol. 3, no. 3, (June 1995), pp. 226–244.
[13] H.-P. Schwefel, L. Lipsky, Impacst of self-similar On/Off traffic on
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
38
delay in stationary queuing models, Performance Evaluation, vol. 43, no. 4, (Mar. 2001), pp. 203–221.
[14]. Z.Zhang, A. Acampora, A heuristic wavelength assignment algorithm
for multihop WDM networks with wavelength routing and wavelength re-use, IEEE/ACM Transactions on Networking, vol. 3, no. 3, (June 1995), pp. 281–288
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
39
Simulative Investigation of 2.5Gbps RZ modulation
format using various optical sources in SOA based
RoF system.
Namita Kathpal
Department of Electronics & Communication Engineering
Deenbandhu Chhotu Ram University of Science &
Technology
Murthal (Sonepat), India
Amit Kumar Garg
Department of Electronics & Communication Engineering
Deenbandhu Chhotu Ram University of Science &
Technology
Murthal (Sonepat), India
Abstract— In this paper, the impact of various optical sources
on the performance of Semiconductor Optical Amplifier based
Radio over Fiber (RoF) system has been analyzed. This RoF
system has been modeled and analyzed using OptiSystem (14.0)
software. The transmission performance of RoF system in terms
of Q-factor, BER and Eye Height at different fiber length using
various optical sources such as VCSEL, Controlled Pump Laser,
Directly modulated Laser and Empirical Laser has been
measured and compared. An improvement in SNR has been
observed by employing VCSEL with Mach-Zehnder modulator
as compared to traditional optical sources in RoF system.
Keywords—VCSEL; Controlled Pump Laser; Directly
modulated Laser; Empirical Laser
I. INTRODUCTION
The constantly increasing bandwidth requirement for fast speed wireless access entails the merging of wireless technology and fiber access technology [1]. Radio over Fiber is the most feasible technology in providing broadband wireless access services in the emerging optical wireless networks [2]. The performance of RoF system rely on several factors such as modulation format, optical modulation, electrical modulation, optical fiber, optical source, bit rate and an optical detector. The principle purpose of optical source is to provide electrical to optical conversion. The responsibility of Laser diode is to modulate the RF signal with light signal. The ample demand for RF transmission through fiber include efficient bandwidth, efficient RF to optical conversion and minimum distortion [3]. There are several Laser Diode for RF to IF conversion i.e. Vertical-Cavity surface emitting Laser, Directly modulated Laser, Controlled Pump Laser and Empirical Laser. The use of Laser as an optical source in RoF system enables the transmission up to multi-gigahertzes. In this paper, the performance of various optical sources at different fiber length has been analyzed in terms of performance metrics such as BER, Q-factor and Eye Height.
II. SIMULATION SETUP
The simulation setup of RZ modulation format based RoF system is shown in Fig. 1. The simulation parameter used in the simulation setup is listed in Table 1. In the downlink transmission, the central station is designed of two signal gen-
TABLE I. SIMULATION PARAMETERS
Names of Parameter Values Units
RZ Bitrate 2.5 Gbps
Light Signal 193.1 THz
Pump Signal 193.13 THz
MZM Extinction Ratio 30 dB
SOA Length 500 µm
SOA Width 3 µm
SOA Height 0.08 µm
EDFA Length 5 m
Fiber Attenuation 0.2 dB/km
Fiber Dispersion 16.75 ps/nm-km
Fiber Length 10 to 50 km
Fig. 1 Simulation setup
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
40
-erators i.e. light signal and Pump signal. At transmitter, twolaser diodes emit light at frequency 193.1 THz and 193.13THz which are provided to Mach-Zehnder Modulator. Thesemodulated signals are multiplexed by WDM multiplexer.These multiplexed signals are amplified by SOA followed byEDFA and transmitted over single mode fiber to base station.The downlink data signal is passed through PIN photodetectorto extract the transmitted signal. The resulting electrical signalis filtered and applied to BER analyzer for analyzing thedownlink signal.
III. RESULTS AND DISCUSSION
The eye diagram of various optical sources at the output of
low pass Bessel filter is examined by BER analyzer as shown
in Fig. 2 (a)–(d). The performance of RZ modulation based
RoF system has been analyzed by Q-factor, BER and Eye
Height. Q-factor describes the quality of signal transmission.
Bit error rate measures the probability of bit errors to the total
number of transmitted bits.
(a) (b)
(c) (d)
Fig. 2 Eye Diagram of RZ modulation format based RoF
system at 20 km fiber length utilizing
(a) Vertical Cavity Surface Emitting Laser
(b) Controlled Pump Laser
(c) Directly Modulated Laser
(d) Empirical Laser
Comparative analysis between various optical sources at
different fiber length is listed in Table. II-V.
TABLE II. INVESTIGATION OF VCSEL PERFORMANCE AT DIFFERENT
FIBER LENGTH
Fiber
Length
(km)
Vertical Cavity Surface Emitting Laser
Q-factor BER Eye Height
10 43.21 0 0.51
20 30.75 3.56e-208 0.29
30 5.13 1.25e-8 0.05
40 11.23 6.43e-30 0.05
50 5.97 2.53e-10 0.01
TABLE III. INVESTIGATION OF CONTROLLED PUMP LASER
PERFORMANCE AT DIFFERENT FIBER LENGTH
Fiber
Length
(km)
Controlled Pump Laser
Q-factor BER Eye Height
10 9.46 1.43e-22 0.41
20 10.49 4.57e-27 0.22
30 6.65 1.92e-12 0.08
40 4.84 2.92e-7 0.02
50 3.69 5.33e-5 0.008
TABLE IV. INVESTIGATION OF DIRECTLY MODULATED LASER
PERFORMANCE AT DIFFERENT FIBER LENGTH
Fiber
Length
(km)
Directly Modulated Laser
Q-factor BER Eye Height
10 9.73 1.07e-23 0.40
20 8.17 1.40e-17 0.02
30 4.75 1.24e-7 0.05
40 3.89 1.45e-5 0.01
50 3.07 2.60e-4 0.0007
TABLE V. INVESTIGATION OF EMPIRICAL LASER PERFORMANCE AT
DIFFERENT FIBER LENGTH
Fiber
Length
(km)
Empirical Laser
Q-factor BER Eye Height
10 56.56 0 0.44
20 10.51 3.28e-26 0.26
30 4.93 4.69e-8 0.05
40 7.78 6.81e-16 0.05
50 3.29 9.09e-5 0.002
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
41
Fig. 3 Q-factor versus Fiber Length
Fig. 4. Eye Height versus Fiber Length The Q-factor with the VCSEL at 20 km fiber length is calculated as 30.75 and reduced to 10.51 using Empirical Laser, which again reduces to 10.49 using Controlled Pump Laser, which is further, reduces to 8.17 in the case of directly modulated Laser. From Fig. 3 and Fig. 4, it is observed that the Eye Height, Eye Opening and Q-factor decreases with increase in propagation distance. It is revealed from Fig. 3 and 4 that VCSEL with RZ modulation format offers superior performance as compared to other optical sources.
IV. CONCLUSION
This paper presents a RZ modulation format based RoF
system and evaluate the performance for 2.5Gbps
communication link. The presented link has been simulated
under various fiber lengths to analyze the performance of
different Lasers to determine the optimum system design for
long haul communication.
References
[1] D. Wake, A. Nkansah, N. J. Gomes, S. Member, G. De Valicourt, R. Brenot, M. Violas, Z. Liu, F. Ferreira, and S. Pato, “A Comparison of Radio Over Fiber Link Types for the Support of Wideband Radio Channels,” Journal of Lightwave Technology, vol. 28, no. 16, pp. 2416–2422, 2010.
[2] V. Sharma, A. Singh and A. K. Sharma, “Simulative investigation of nonlinear distortion in single- and two-tone RoF systems using direct- and external-modulation techniques,” Optik - International Journal for Light and Electron Optics, vol. 121, no. 17, pp. 1545–1549, 2010.
[3] C. Lin, J. Chen, P. Peng, C. Peng, W. Peng, B. Chiou and S. Chi, “Hybrid optical access network integrating fiber-to-the-home and radio-over-fiber systems,” IEEE Photonics Technology Letters, vol. 19, no. 8, pp. 610-612, April 15, 2007.
[4] T. Katsuzama, “Development of Semiconductor Laser for Optical Communication,”Sei Technical Review, Number 69, October 2009.
[5] Q. Wang, F. Zeng, S. Blais, and J. Yao, "Optical ultrawideband monocycle pulse generation based on cross-gain modulation in a semiconductor optical amplifier," Optics Letters, vol. 31, no. 21, pp. 3083-3085, 2006.
[6] K. Y. Cho, Y. J. Lee, H. Y. Choi, A. Murakami, A. Agata, Y.Takushima, and Y. C. Chung, “Effects of reflection in RSOA-based
WDM-PON utilizing remodulation technique,” J. Lightw. Technol.,
vol. 27, no. 10, pp. 1286–1295, May 2009. [7] A.Loayssa, J.M. Salvide, D. Benito and M.J. Garde, “Novel Optical
Single - Sideband Suppressed Carrier Modulator using Bidirectionally Driven Electro-optic Modulator", IEEE Technical Digest: Microwave Photonics 2000 conference, Alwyn Seeds, pp. 117 - 120, Oxford, 2000.
[8] A.Carena,V.Curri and P.Poggiolini, “On the Optimization of Hybrid Raman/Erbium-Doped Fiber Amplifiers‟, IEEE Photonics Technology Letters, vol. 13, no. 11, pp. 1170-1172, Nov. 2001.
[9] R.S. Kaler, “Simulation of 16 ×10 Gb/s WDM system based on optical amplifiers at different transmission distance and dispersion” Optik, pp.1654– 1658, 2012.
[10] Chien-Hung Yeh, Kuo Hsiang Lai, Ying Jie Huang Chien-Chung Lee and Sien Chi. “Hybrid L-Band Optical Fiber Amplifier Module with Erbium-Doped Fiber Amplifiers and Semiconductor Optical Amplifier”, Japanese Journal of applied Physics, vol. 43, pp. 5357–5358, 2004.
[11] N. Kathpal and A. K. Garg, “Performance Analysis of Multitone RoF system using DPSK based Optical Modulators,” International Journal of Electronics, Electrical and Computational System, vol. 6, no. 7, pp. 532–535, 2017.
[12] N. Kathpal and A. K. Garg, “Performance analysis of Radio over Fiber system using Direct and External Modulation Schemes,” International Journal of Engineering Technology, Management and Applied Sciences, vol. 8, no. 4, pp. 172–175, 2017.
[13] N. Kathpal and A. K. Garg, “Mitigation of Dispersion Effects for better Quality of Transmission in RoF system – A Review,” International Journal of Engineering Technology, Management and Applied Sciences, vol. 5, no. 3, pp. 9–13, 2017.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
42
Abstract—to route data efficiently from source to
destination is one of the stimulating jobs in wireless sensor
network. And also in Wireless Sensor Networks (WSNs) due to
increasing demand for real-time applications has made the
Quality of Service (QoS) based communication protocols
becomes interesting and most researched topic. Bandwidth and
delay constraints are Quality of Service (QoS) requirements
which are required. and For the different QoS based
applications of WSNs raise substantial challenges. Energy
consumption is also a prominent and critical issue faced by
wireless sensor networks. When the sensors communicate with
each other the maximum amount of energy is consumed. In
order to develop the lifetime of the network, energy should be
used in an efficient manner. Therefore we need energy efficient
routing mechanisms .The well-known low-energy adaptive
clustering hierarchy (LEACH), that is Cluster-based routing
techniques are used to achieve scalable solutions and extend the
network lifetime until the last node dies (LND).
Index Terms— WSN, Localization of WSN Nodes, Design
challenges of WSN, Schemes of Node Deployment
I. INTRODUCTION
In recent years Wireless Sensor Networks (WSNs) have achieved worldwide attention, particularly with the proliferation of Micro-Electro-Mechanical Systems (MEMS) technology, which has facilitated the development of smart sensors [1]. In current years, wireless communications technology is growing rapidly, and the miniaturization and low cost of sensing devices, have give boost to the development of wireless sensor networks (WSNs)[2].The limited and generally irreplaceable power sources of the sensor nodes is One of the major constraints of WSNs. Still, it is impractical to replace, in many applications, the sensor nodes as they work under harsh environment. Therefore, for long run operation of WSNs reducing energy consumption of the sensor nodes is considered as the most critical challenge. For designing energy saving protocols which should have features of low-power radio communication hardware, energy-aware MAC protocols, etc.[3] Extensive researches have been carried out. Wireless Sensor Networks (WSN) refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. Like pollution levels, temperature, sound, humidity, wind speed and direction, pressure, etc environment conditions, WSNs govern. In general quite a substantial amount of data, WSN may generate If data fused could be used, the throughput could be used [4].
1.1 Multi-hop routing algorithms for wireless sensor
networks
The basic function of a routing algorithm is to select the path
from a set of available paths that is most efficient based on a
specific criterion. Intuitively, to maximize the WSN’s
network lifetime, the path that achieves minimum power
consumption while ensuring fair power consumption among
individual nodes should be used. multi-hop routing
algorithms of WSN a lot of effort has focused, and many
algorithms have been proposed for this. flat multi-hop routing
algorithms and hierarchical multi-hop routing algorithms are
the broad classification.
1.1.1 Flat multi-hop routing algorithms
In Fig. 1, an picture representation of how flat multi-hop
routing algorithms are used to send data is shown. In the
illustration, to communicate over a bounded area within its
maximum transmission range to other sensor nodes each
sensor node has the ability, and an arrow’s thickness is
proportional to the quantity of facts being transmitted over
that corresponding link. In practice, utilization of link act
very differently between different routing algorithms.
Fig. 1: Flat multi-hop routing sending data.
1.1.2 Hierarchical multi-hop routing
To choose minimum power consuming paths they used
power aware metrics and Flat multi-hop routing algorithms
are excellent in choosing this. However data collected from
the WSN which is of correlated nature it cannot take the
complete advantage. The application scope of the WSN (e.g.,
temperature readings collected from geographically nearby
locations have a high likelihood of changing into similar),and
The comparatively high node density of the WSN create
information aggregation a really attractive procedure in
WSN. ranked multi-hop routing algorithms with success
utilize the info aggregation to decrease the degree of
information flowing within the network. In class-conscious
multi-hop routing algorithms, detector nodes assume
completely different roles, which may be modified with time.
As given in Fig. 2. LEACH may be a two-layered
class-conscious multi-hop routing algorithmic rule. Every
node will play the role of a Cluster Head (CH) or Cluster
Member (CM) [5] [8]
Fig. 2: Two layered Hierarchical multi-hop routing [5].
1.2 Multipath routing
Energy Efficient Routing Protocols for
Wireless sensor network
Dr. Mukesh Singla Prof. & Director M.S.I.E.T, Kalanaur
email id: [email protected]
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
43
Single path routing protocols in sensor network are designed
to discover a single path between a source/destination pair.
From the other hand, multipath routing consists of finding
multiple paths between the source and destination nodes.
These multiple paths can be used to solve some trade-off in these
networks and fulfilled with the vibrant nature of WSNs .
1.2.1 Benefits of multipath routing
As mentioned in the introduction, multipath routing protocols
can provide load balancing, fault tolerance, bandwidth
aggregation, and reduced delay. Below, we discuss how to
provide each of these benefits in multipath routing.
1.2.1.1 Load balancing
As explicit in, one amongst the explanations that classical
multipath routing has been explored is to supply load
equalisation. Load balancing can be achieved by splitting the
traffic across multiple route. This quality of multipath routing
is implicable to WSNs. in the sensor network energy
utilization across nodes, potentially resulting in longer
lifespans Load balancing can spread. Furthermore, load
balancing also helps in avoiding congestion and bottleneck
problems .
1.2.1.2 Reliability and fault tolerance
In WSNs Reliability is a big matter, because data
transmission is subject to lost due to several reasons: several
types of interference, media access conflicts, network
topology changes, etc. These reasons affect the wireless
radios to correctly decode the wireless signals. Developing
multipath routing one among the explanations behind is to
supply route failure protection, and increase resiliency to
route failures. Discovering and maintaining multiple paths
between the source and destination pair improves the routing
performance by providing alternative routes. When the
primary path fails, an alternative path will be used to transfer
the data during this case the multiple methods don't seem to
be used at the same time. for knowledge routing Multiple
methods is used at the same time. coinciding multipath
routing can be accustomed improve responsibility.
1.2.1.3 Highly aggregated bandwidth
For a connection Routing over a single path may not provide
enough bandwidth; Bandwidth may be limited in a WSN.
However, if data are routed over multiple paths
simultaneously the overall bandwidth of the paths may
satisfy the bandwidth requirement of an application.
1.2.1.4 Minimizing end to end delay
By assuming that the paths between the source and
destination pair are node disjoint paths where correlation
between the paths is very low, and there is no route coupling
between different routes (for example, through the using of
directional antennas this could be achieved), the end to end
delay can be minimized by dividing the data (to be sent) into
a number of segments and using multiple paths to route
segments simultaneously to the destination.
1.3 Problems with multipath routing
A shared wireless channel to communicate Nodes in the
wireless sensor network use. neighboring nodes must content
for the channel This means. When the channel is busy by a
transmission node, neighboring nodes hear the transmission
and square measure blocked from receiving information from
alternative nodes. to boot, reckoning on the below egg laying
mack protocol, neighboring nodes could ought to defer their
transmission until the channel is free. Even once varied
channels square measure used, attributable to the interference
the standard of neighboring transmission is also degraded.
Now, contemplate the utilization of a multipath routing,
wherever the multiple ways square measure used at the same
time. Even, the multiple routes square measure node-disjoint
paths; transmissions over the routes could interfere if some
nodes square measure within the transmission vary of every
alternative. This downside is termed route coupling. once 2
routes square measure situated physically shut enough to
interfere with one another throughout digital communication
Route coupling happens. for access to the wireless channel
they share and may find yourself activity worse than one path
protocol Nodes in those 2 routes square measure perpetually
competitory As a result. Thus, for improved performance
node-disjoint routes aren't a decent condition
II. LITERATURE REVIEW
Enan A. Khalil et al. in 2011[1] the main challenges in
designing and planning the operations of Wireless Sensor
Networks (WSNs) are to optimize energy consumption and
prolong network lifetime. Like as the well-known
low-energy adaptive clustering hierarchy (LEACH)
Cluster-based routing techniques, are used to attain scalable
solutions and extend the network lifespan until the last node
dies (LND). Also to address energy-aware routing challenges
as meta-heuristics by designing intelligent models that
collaborate together to optimize an appropriate energy aware
objective function in recent years evolutionary algorithms
(EAs) have been successfully used. On the other hand, some
protocols, are concerned with another objective: extending
the stability time until the first node dies (FND), such as
stable election protocol (SEP). Often, between extending the
time until FND and the time until LND there is a tradeoff. To
our data, no try has been created to get a more robust
compromise between the soundness time and network period
of time. the foremost vital characteristic of the Semitic deity
(i.e., the objective function) of The design,This paper
reformulates, so as to obtain a routing protocol that can
provide more robust in terms of network stability period
results than the existing heuristic and meta-heuristic
protocols, lifetime, and energy consumption. with efficient
energy utilization routing protocol Better tradeoff between
the lifespan and the stability period of the network, which can
guarantee An evolutionary-based is projected. WSN models
are evaluated and compared against the LEACH, SEP, and
one of the existing evolutionary-based routing protocols,
hierarchical clustering-algorithm-based genetic algorithm
(HCR) To support this claim, extensive simulations on 90
homogeneous and heterogeneous.
Ahmed E.A.A. Abdulla et al. in 2012[2] Power-aware routing
in wireless sensor networks (WSNs) focuses on the crucial
problem of extending the network lifetime of WSNs, which
are limited by low-capacity batteries. However, most of the
contemporary works fail to resolve the hotspot problem,
which is the isolation of the sink node due to the power
exhaustion of sink close-by nodes. To address this issue
through a hybrid approach that combines two routing
strategies Author propose a solution, flat multi-hop routing
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
44
and hierarchical multihop routing In this paper. The former
aims to reduce the whole power consumption within the
network, and also the latter tries to decrease the number of
traffic by utilizing knowledge compression. Author
mathematically evaluate the power consumption of our
proposed algorithm, then author demonstrate through
extensive simulations that the proposed scheme is able to
extend the network lifetime by alleviating the hotspot
problem.
Md Azharuddin et al. in 2014[3] Conservation of energy and
fault tolerance are two major issues in the deployment of a
wireless sensor network (WSN). For an oversized scale WSN
style of agglomeration and routing algorithms ought to
incorporate each these problems for the long-term operation
of the network. Author recommends in this paper distributed
clustering and routing algorithms jointly referred as DFCR.
to be energy efficient and fault tolerant The algorithm is
shown. Due to hasty failure of the cluster heads (CHs) The
DFCR uses a distributed run time recovery of the sensor
nodes. It takes care of the device nodes that don't have any
CH among their communication vary. on the proposed
algorithm Author performs extensive experiments using
various network scenarios. With the existing algorithms the
experimental outcome are compared to demonstrate the
strength of the algorithm in terms of various performance
metrics.
A.SARANYA et al. in 2015[4] In Wireless Sensor Networks,
Sensors are generally battery powered devices. This network
is used to gather various kinds of information to Base station
(BS). Of procedure management, storage capability, energy
provides are the vital problems in their energy constraint they
contain. to maximize the network time period, author needn't
solely to attenuate total energy consumption and conjointly
balance WSN load. A new Fuzzy based General Self
organized Tree based Energy Balance routing protocol
proposed in this paper, which builds a routing tree using a
process where, for each round BS assigns a root node and
broadcast to all sensor nodes. Equally by considering solely
itself and its neighbor’s info every node selects its parent,
therefore creating a dynamic protocol. projected protocol
performance is healthier than alternative protocols
Simulation outcome show that
Jalel Ben-Othman et al. in 2010[5] in Wireless Sensor
Networks (WSNs) The increasing demand for real-time
applications has made the Quality of Service (QoS) based
communication protocols an interesting and hot research
topic. for the dissimilar QoS primarily based applications of
WSNs raises important challenges Satisfying Quality of
Service (QoS) needs (e.g. information measure and delay
constraints).More exactly, the networking protocols need to
survive up with energy constraints, while providing precise
QoS guarantee. Hence, enabling QoS applications in sensing
element networks in numerous layers of the protocol stack
needs power and QoS awareness. In several of those
applications (such as transmission applications, or time
period and mission crucial applications is mixed of delay
sensitive and delay tolerant traffic ), the network traffic.
Hence, QoS routing becomes a very important issue. during
this paper, author propose AN Energy economical and QoS
aware multipath routing protocol (abbreviated shortly as
EQSR) that maximizes the network period through
reconciliation energy consumption across multiple nodes, to
allow delay sensitive traffic to reach the sink node within an
acceptable delay uses the concept of service differentiation,
reduces the end to end delay through spreading out the traffic
across numerous paths, and increases the throughput through
introducing data redundancy. To work out the most effective
next hop through the routes construction part EQSR uses the
residual energy, node on the market buffer size, and ratio
(SNR). EQSR protocol employs a queuing model to handle
both real-time and non-real-time traffic Relayed on the
notion of service differentiation. With the MCMP
(Multi-Constraint Multi-Path) routing protocol author
calculate and associate the performance of our routing
protocol By means of simulations. Achieves our protocol
lower average delay Simulation outcome have shown that, a
lot of energy savings, and better packet delivery quantitative
relation than the MCMP protocol.
Basma M. Mohammad El-Basioni et al. in 2011[6] Because
sensor nodes typically are battery-powered and in most cases
it may not be possible to change or recharge batteries, the key
challenge in Wireless Sensor Networks (WSNs) design is the
energy-efficiency and how to deal with the trade-off between
it and the QoS parameters required by some applications. in
terms of lifetime, delay, loss percentage, and throughput, and
proposes The QoS of an energy-efficient cluster-based
routing protocol called Energy-Aware routing Protocol
(EAP) some modifications on it to enhance its performance
studies in this paper. Better characteristics in terms of packets
loss, delay, and throughput, but slightly affects lifetime
negatively The modified protocol offers. in terms of packet
loss percentage by on average nearly almost 93.4%
Simulation results showed that The updated protocol
considerably outperforms EAP.
Harish Kumar et al. in 2013[7] Energy consumption is
prominent and critical issue faced by wireless sensor
networks. The maximum quantity of energy is consumed
once the sensors communicate with one another. That’s why
energy efficient routing mechanisms are required. In this
paper, a routing scheme based on the fisheye state routing
with a difference in route selection mechanism has been
proposed to ensure the reduction in the overall energy
consumption of the network. This format is termed as
Energy-Aware Fisheye State Routing (EA-FSR). It is
simulated considering varied parameters victimisation
QualNet5.0. Performance of EA-FSR has been compared
with the first optical lens state routing algorithmic rule that is
additionally simulated within the same setting. varied
parameters For comparison like end-to-end delay average,
energy consumption and outturn are thought-about.
J. Gnanambigai et al. in 2014[8] the fastest growing
technology that would dominate the future world of wireless
communication is Wireless Sensor Networks (WSNs). The
essential issue in WSNs is energy. Energy ought to be
employed in Associate in Nursing economical manner, so as
to enhance the period of time of the network. to enhance
energy potency of wireless sensing element networks many
routing protocols has been developed. The routing protocol
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
45
could also be standard sort or hybrid sort. the benefits of 2
totally different protocols the hybrid sort integrates. In this
paper, a new hybrid routing protocol called Quadrant Based
Low Energy Adaptive clustering Hierarchy (QB-LEACH ) is
developed where lifetime improvement is vital. This protocol
integrates the Quadrant based mostly Directional
Routing(Q-DIR), AN Ad-hoc routing algorithmic rule and
Low Energy adaptational clump Hierarchy (LEACH), a
routing algorithmic rule for WSNs. The performance nature
of the protocol is evaluated and discovered that this protocol
shell the opposite in terms of energy conservation and
network amount.
Table 1 Routing Protocols in WSN’s
III. Conclusion
A hybrid multi-hop routing algorithm by combining flat and
hierarchical multi-hop routing algorithms can resolve the
problem of the isolation of the sink caused by the battery
exhaustion of nodes around it. The hybrid multihop routing
algorithm is a promising solution for the hotspot problem and
extending the network lifetime. A distributed cluster and
routing formula referred to as DFCR for wireless device
networks that are energy economical similarly as fault
tolerant. A General Self organized which is novel Fuzzy
based, Tree based Energy Balance routing protocol proposed,
which builds a routing tree using a process where, for each
round BS assigns a root node and broadcast to all sensor
nodes. Equally each node selects its parent by considering
only itself and its neighbor’s information, thus making a
dynamic protocol. And the results show that proposed
protocol performance is better than other protocols. an
Energy economical ANd QoS aware multipath routing
protocol (abbreviated shortly as EQSR) that maximizes the
network period of time through equalisation energy
consumption across multiple nodes. within an acceptable
delay to reach the sink node Uses the theory of service
differentiation, reduces the end to end delay through
spreading out the traffic across multiple paths, and increases
the throughput through introducing data redundancy. EQSR
uses the residual energy, node available buffer size, and
Signal-to-Noise Ratio (SNR) to predict the best next hop
through the paths construction phase. EQSR protocol
employs a queuing model to handle both real-time and
non-real-time traffic, Based on the concept of service
differentiation.
References
[1]. Bara’a A ,Enan A. Khalil. ,Attea,”Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks”, Swarm and Evolutionary Computation, 2011 ,pp. 195–203
[2]. Ahmed E.A.A. Abdulla , Hiroki Nishiyama, Nei Kato,” Extending the lifetime of wireless sensor networks: A hybrid routing algorithm”, Computer Communications 35,2012, pp. 1056–1063
[3]. Md Azharuddin, Pratyay Kuila, Prasanta K. Jana, ”Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks”, Computers and Electrical Engineering, 2014, pp.1-7
[4]. A.SARANYA, R.SENTHIL KUMARAN, Dr. G.NAGARAJAN, ”Enhancing Network Lifetime using Tree Based Routing Protocol in Wireless Sensor Networks”, INTERNATIONAL CONFERENCE ON ELECTRONICS AND COMMUNICATION SYSTEM , 2015, pp.1-10
[5]. Jalel Ben-Othman, Bashir Yahya, ”Energy efficient and QoS based routing protocol for wireless sensor networks”, J. Parallel Distrib. Comput. 70,2010, pp. 849_857.
[6]. Basma M. Mohammad El-Basioni , Sherine M. Abd El-kader , Hussein S. Eissa , Mohammed M. Zahra, ”An Optimized Energy-awareRouting Protocol for Wireless Sensor Network”, Egyptian InformaticsJournal ,2011- 12, pp.61–72.
[7]. Harish Kumar , Harneet Arora , R.K. Singla, ”Energy-Aware Fisheye Routing (EA-FSR) algorithm for wireless mobile sensor networks”, Egyptian Informatics Journal , 2013- 14, pp.235–238.
[8]. J. Gnanambigai, N. Rengarajan, N.Navaladi, ”A CLUSTERING BASED HYBRID ROUTING PROTOCOL FOR ENHANCING NETWORK LIFETIME OF WIRELESS SENSOR NETWORK”, 2nd International Conference on Devices, Circuits and Systems (ICDCS), 2014, pp.1-4
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
46
Hundred Percent Secure And Pure Steganography
without Password Protection
Alok Sharma Nidhi Sharma Dr.V.K Srivastva
Ph.D Scholar Assistant Professor Professor
Dept of CSE, Baba Mast Nath Dept of CSE, TIT&S, Bhiwani Dept of CSE, Baba Mast Nath
University,Rohtak Bhiwani, India University,Rohtak
Rohtak,India [email protected] Rohtak, India
Abstract—
In this digital world, various security schemes and
algorithm are intended to protect information from intruder,
attacker and unwanted parties. This is the example of pure
steganography which donot take help of cryptography and
password protection. Normally steganography without the help
of cryptography is not considered hundred percent secure.
Along with cryptography steganographic algorithms available
in market are protected with password security. In this paper
steganographic algorithm witch is hundered percent secure and
impossible to break by any tool available in the market is
explained which can not be detected by any technique or tools
available in the market with attacker , unwanted parties and
intruder. In this paper, a Algorithm to Secure data in online
transmission is proposed which is example of pure
steganography and provides hundered percent security to
online data.
1. INTRODUCTION
Transfer of information and sharing of information to distant locations has increased to large extent in todays digital world. So it has become compulsory to secure this information transferred over internet. There are lots of techniques available to secure information transferred over internet example public key cryptography, private key cryptography, hashing algorithms and steganographic techniques.[1] This algorithm will cover main advantage of steganographic techniques to keep the existence of secret message unrevealed along with the benefit of making it impossible for intruder , attacker and third party to retrieve even a single bit of secret message . This algorithm is NP-Hard algorithm on network for intruder, attacker and third party but polynomial at receiver end.[2]
2.DESCRIPTION OF ALGORITHM
In this algorithm both receiver and sender first mutually
agrees on one BIT Table with values 0 and 1 in table and
retains one (Same) copy of BIT Table with each and
maintains it secret as the case of physically key exchange
method in key based algorithm in cryptography where both
parties take their key physically (By transportation) not
online.[3] This is the case of steganography as this BIT
Table is made from random selection of LSB (least
significant bits )from binary values of color bit values of
image. We are aware that all images are made up of color
and every pixel has color intensity for RGB (red green blue)
between 0-255.These color intensity comes out as binary
values. So above mentioned BIT Table is made from binary
values of color of image. As we know all communication on
computer is in binary form that is 0 and 1. So message to be
sent is converted into binary and it is spread in bit table such
that 0 of message match with 0 of bit table and 1 with 1 as
explained in example given below. After spreading binary
bits of message in bit table locations of bit table are stored
where bits of message are spread and location array with
these locations is transferred over network as explained in
example given below. On the receiver end values in location
array location are retrieved from bit table available at
receiver end received initially physically. [4]These values
are converted into character form which gives original
message. Important point in this algorithm is that it is two
way algorithm as sender can become receiver any time and
receiver can become sender any time as both parties have
same bit table mutually agreed and received physically (By
Transporation) as keys are exchanged in key based
algorithms in the starting session.[5]
3. ALGORITHM TO PROVIDE HUNDER PERCENT
SECURITY IN ONLINE TRANSMISSION
Encryption Algorithm( Explained in Fig 1 below)
1.Retain copy of BIT Table mutually agreed upon byreceiver and sender in starting of session
2. Secret message is taken
3.Secret message is converted into binary
4. the binary values of secret message are spread such that 0and 1 of message match with 0 and 1 of BIT Table
5.The location of BIT Table are stored where digits of secretmessage are stored in their sequence.
6Location array of BIT Table locations is made
7 The location array is transferred over internet to receiver.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
47
Decryption Algorithm (Explained in fig 1 below)
1Read those values on Reciever side of BIT Table as given in location array
2These values are Stored in sequence
3These values are Converted from binary to text form
4On conversion Original message is retrieved.
5Decryption is complete.
EXAMPLE
Suppose original messageis numeric 24 which we want to
send online using this algorithm. First we convert 24 into binary, the binary is
0 0 0 1 1.
Suppose in the starting receiver and sender mutually agrees
upon BIT Table given below, copy of given below bit table
is retained by both (receiver and sender) in start of session
physically as key exchange mechanism in key based
algorithm
Reciever end Sender end
1 1 0 1 1 0
0 1 0 0 1 0
1 0 1 1 0 1
1 0 0 1 0 0
Sender spread the message in underlined locations that is
1 1 0
0 1 0
1 0 1
1 0 0
So location array of BIT Table is
1,3 first row third column
2,1 second row first column
2,3 second row third column
3,1 third row first column
3,3 third row third column
We send this location array online over network and at receiver end we read the values of BIT Table at locations
given in Location array which is
1,3 first row third column value retrieved is 0
2,1 second row first column value retrieved is 0
2,3 second row third column value retrieved is 0
3,1 third row first column value retrieved is 1
3,3 second row third column value retrieved is 1
When these values are changed to character gives 24 which
is original message.
4. Case 2 Application of This algorithm in Oral TelephonicConversation Even this basic algorithm can be used to secure oral
communication on telephone. Now a days this is major
problem that our secret plans of defense, politicians,
scientists ect are Intercepted and privacy of message is
spoiled completely. The necessary information goes in the
hands of illegal persons. This way basic need of
communication to provide integrity and security is spoiled.
To overcome this problem this case 2 application of above
algorithm is suggested which will provide hundred percent
integrity and security to oral communication.
5. Princple:
In this algorithm application both receiver and sender has to
mutually agree on one alpha-numeric table in the starting of
session. In this table we write numeric values numbered one
to thirty and above these numbers we write alphabets
randomly rather than in sequence. One copy of this alpha-
numeric table is mutually agreed upon by both receiver and
sender in the starting of session, taken physically by
transportation not online as key-exchange mechanism in key
based algorithms in cryptography. This is the basic condition
to take this alpha-numeric table physically to make session
hundred percent secure life long time. Once alpha-numeric table is exchanged we have to just send
message of digits or by oral communication. Example:
Suppose we want to send excellent
And alpha-numeric table agreed in starting is
v z x y w s r u t o
1 2 3 4 5 6 7 8 9 10
q p l k n m j i
11 12 13 14 15 16 17 18
h g f e d c b a
19 20 21 22 23 24 25 26
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
48
q y p m
27 28 29 30
To use this algorithm we have to send
By message or orally
22, 3, 24, 22, 13, 13, 22, 15, 9
And receiver will pick the values above these numeric values That is
22 numeric gives e
3 numeric gives x
24 numeric gives c
22 numeric gives e
13 numeric gives l
13 numeric gives l
22 numeric gives e
15 numeric gives n
9 numeric gives t
On combining this original message
Is excellent
6. SECURITY ANALYSIS AND HARDNESS OF
ALGORITHM
There are three type of hardness of any algorithm. i.e.
Polynomial algorithm, NP-Complete algorithm, NP-Hard
algorithm.
1. Polynomial algorithm
This type of algorithm are solvable in polynomial
time , are easy and have defined solution. This type
of algorithms can be solved by computer in defined time without any difficulty.[6]
2. NP-Complete algorithm
This type of algorithms are harder than
polynomial algorithms. There exists only one
solution which can be got through hit and trial
method by trying all possible solutions. This
method take very high processing time of CPU as
computer has to try all possible solutions.
3. NP-Hard
This type of algorithm are hardest as no solution exists for such algorithms and can not be solved to
get the solution. Such algorithm take infinite time means impossible to solve .[7]
So the above mentioned algoritms case 1 and case 2 are case
of NP-Hard for intruder/attacker/hacker as they cannot solve
these algorithms to get the original message by any means.
The reason behind this is that by online medium we are
sending only address values from where we have to pick
values but not the values. The message values data is
transported physically in the starting of algorithm as is the
case with key based algorithms in cryptography. On the
other hand at the receiver side it is a polynomial algorithm as
we have both data values and data addresses.[8] Secondly once image/BIT Table/ Alpha-numeric table transported physically in starting of session once gives
hundred percent secure communication for whole life. This
session can be established any time in life. Thirdly this is a two way communication. Reciever and sender can mutually exchange each other any time. This analysis confirms that it is hundred percent secure
algorithm to transfer data over internet with zero cost of
algorithm and within approach of every civilian. What is
required to use this algorithm is internet connection with
basic knowledge of computer fundamental.[9]
7 CONCLUSION
This algorithm confirms hundred percent security and
integrity of data transferred using this algorithm. This algorithm is free of cost as any internet user with basic
knowledge of computer fundamental can use it without any
external requirement.
REFERENCES
[1] https://books.google.com/books?id=Z8WiAwAAQBAJ
[2] http://www.ijsce.org/attachments/File/v3i5/E190011351 .pdf
[3] J. Fridrich, M. Goljan, and R. Du, “Detecting LSB steganography in color, and gray-scale images,” IEEE Multimedia, vol. 8, no. 4, pp. 22– 28, Oct. 2001.
[4] D. Wu and W. Tsai, “A steganographic method for images by pixel value differencing,” Pattern Recognit. Lett., vol. 24, pp. 1613–1626, 2003
[5] Z. Ni, Y.Q. Shi, N. Ansari, W. Su, “Reversible data hiding” , IEEETransactions on Circuits and Systems for Video Technology , 16 , 3,PP 354–362,2006.
[6] X. Li, T. Zeng, and B. Yang, “Detecting LSB matching by applying calibration technique for difference image,” in Proc. 10th ACM Workshop on Multimedia and Security, Oxford, U.K, pp. 133–138, 2008.
[7] D. Wu and W. Tsai, “A steganographic method for images by pixel value differencing,” Pattern Recognit. Lett., vol. 24, pp. 1613–1626, 2003
[8] L.M. Marvel, C.G. Boncelet , & C. Retter, “Spread Spectrum Steganography”, IEEE Transactions on image processing, 8,8, PP 160-178, 2007.
[9] Y.B. Mao,G. Chen. S.G. Lian,”A novel fast image Encryption scheme based on the 3D chaotic baker map,”Int. j. Bifurcate Chaos, vol. 14, pp.3613-3624,2004.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
49
Stock Market Data Analysis using Apache
Abhinav Juneja, Shubham Jain, Ekta Gandhi
Department of Computer Science and Engineering,
BM Institute of Engineering and Technology, Sonepat, Haryana, India
Abstract---It this era of digitization, a relatively new
term has started to emerge in the field of Information
Technology. The term is BIG DATA. Big Data refers to any
data that is too large to be processed by traditional database
systems effectively. It requires a lot of computat
to handle such large amounts of data generated everyday
from various sources such as Social Media, Traffic Control,
Weather, Navigation, Stock Market etc.Big Data analysis
allows us to obtain useful information from such gigantic
amounts of data. The data available can be structured or
semi-structured or unstructured.Stock Market data is
Structured data generated from the trading on the stock
market. Every day millions of shares are traded on an
average day at any stock exchange in the world. An
this data requires a lot of computational power and that too
takes a lot of time. Apache Hadoop offers a framework
based on Distributed computing which helps us analyze that
data in a cost effective manner and at a much faster rate.
1. INTRODUCTION
Big datais an evolving term that describes anyvoluminous amount of structured, semistructured andunstructured data that has the potential to be mined forinformation.
Fig1: big data
II .3V of BIG DATA
A.Volume
Volume means amount of data stored. Data nowadaysis more than just text, Large amount of data is createddaily in the form of videos, images. Volume of datastored in enterprise repositories have grown frommegabytes and gigabytes to petabytes. This big volumecomprises Big data
Stock Market Data Analysis using Apache
Hadoop Abhinav Juneja, Shubham Jain, Ekta Gandhi
Department of Computer Science and Engineering,
BM Institute of Engineering and Technology, Sonepat, Haryana, India
digitization, a relatively new
term has started to emerge in the field of Information
Technology. The term is BIG DATA. Big Data refers to any
data that is too large to be processed by traditional database
systems effectively. It requires a lot of computational power
to handle such large amounts of data generated everyday
from various sources such as Social Media, Traffic Control,
Weather, Navigation, Stock Market etc.Big Data analysis
allows us to obtain useful information from such gigantic
a. The data available can be structured or
structured or unstructured.Stock Market data is
Structured data generated from the trading on the stock
market. Every day millions of shares are traded on an
average day at any stock exchange in the world. Analysis of
this data requires a lot of computational power and that too
takes a lot of time. Apache Hadoop offers a framework
based on Distributed computing which helps us analyze that
data in a cost effective manner and at a much faster rate.
Big datais an evolving term that describes any voluminous amount of structured, semistructured and unstructured data that has the potential to be mined for
Data nowadays Large amount of data is created videos, images. Volume of data
stored in enterprise repositories have grown from megabytes and gigabytes to petabytes. This big volume
Big data
B.VelocityThe rapid growth in data and social media explosionhave changed how we used to look at the data. Velocitymeans the speed of data processing. processes such as catching fraud, big data must be usedas it streams into your enterprise in order to maximize itsvalue.
C.VarietyVariety refers to different types of data and data sources.The data variety may vary from structured data tounstructured, semi structured, video, audio, XML etc.This large variety of data represent Big data.
3. PROBLEMS IN BIG DATA PROCESSING
•••• Heterogeneity and Incompleteness
When humans analyse data , they can comfortably
tolerate heterogeneity. But machine analysis algorithms
need homegeneous data for processing. So, before
processing the data for analysis, it must be carefully
structured.
•••• Scale
“Big Data” describes a large database with structured as
well unstructured data. Managing a large database is a
very tedious task.Earlier, this problem was
solved by the processors getting faster but now data
volumes are becoming huge and processors are static.
World is moving towards the Cloud technology, due to
this shift data is generated at a very high rate. New and
efficient storage systems are needed to store this big
amount of data.
•••• Timeliness
The big challenge with large databases is speed. It is very
obvious that, The larger the database, longer it will take
to analyze.Speed of analyzing the data is a big challenge.
•••• Privacy
This is the another big problem with the Big data.
the data privacy issues there are strict laws in some
countries. Privacy is both a technical and social issue
which must be addressed jointly to fulfill the promise of
Big data.
•••• Human Collaboration
In this era of technology , we have advanced
computational models but there are m
computer cannot detect. The field of Big Data is not
Stock Market Data Analysis using Apache
he rapid growth in data and social media explosion have changed how we used to look at the data. Velocity means the speed of data processing. For time-sensitive processes such as catching fraud, big data must be used
in order to maximize its
Variety refers to different types of data and data sources. The data variety may vary from structured data to unstructured, semi structured, video, audio, XML etc. This large variety of data represent Big data.
PROBLEMS IN BIG DATA PROCESSING
Heterogeneity and Incompleteness
When humans analyse data , they can comfortably
tolerate heterogeneity. But machine analysis algorithms
need homegeneous data for processing. So, before
it must be carefully
“Big Data” describes a large database with structured as
well unstructured data. Managing a large database is a
very tedious task.Earlier, this problem was
solved by the processors getting faster but now data
are becoming huge and processors are static.
World is moving towards the Cloud technology, due to
this shift data is generated at a very high rate. New and
efficient storage systems are needed to store this big
e with large databases is speed. It is very
obvious that, The larger the database, longer it will take
to analyze.Speed of analyzing the data is a big challenge.
This is the another big problem with the Big data. Due to
there are strict laws in some
countries. Privacy is both a technical and social issue
which must be addressed jointly to fulfill the promise of
In this era of technology , we have advanced
computational models but there are many patterns that a
computer cannot detect. The field of Big Data is not
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
50
automatic i.e. it needs human intervention. Hence,
Experts from various field have to design the model and
work to constantly improve it for better analysis.
4. HIVE
It is a Data Warehouse Software built in ApacheHadoop for Querying and Managing Large DistributedDatasets. Apache Hiveis a component of HortonworksData Platform(HDP). Hiveprovides a SQLto data stored in HDP. Hiveprovides a database queryinterface to Apache Hadoop.
6. SOLUTION FOR BIG DATA PROCESSING
HADOOP
Hadoop is a Programming framework used to supportthe processing of large data sets in a distributedcomputing environment.
The need of Hadoop emerged with the avalanche ofBig Data. The World Wide Web was generating data at atremendous rate. The data generated had a lot of potentialafter analysis but the cost of doing so was alsotremendous.
The idea for Hadoop evolved in 2003 when Googlepublished a paper named Google File System fby MapReduce: Simplified Data Processing on LargeClusters.
This inspired Doug Cutting and Mike Cafarella tocreate Hadoop, an open source framework called Hadoopwhich offered Big data processing on a Distributedplatform.
Hadoop was designed with a simple writestorage infrastructure. It was initially developed as afilesystem which offered much faster data accession aswell as stored multiple copies of the data providing a failsafe storage solution. HDFS or Hadoop Distributed FileSystem is a reliable storage infrastructure that stores thedata in the form of blocks. These blocks are replicated ondifferent servers for reliable storage.
Hadoop has moved far beyond its beginnings in webindexing and is now used in many industries for a hugevariety of tasks that all share the common theme of lotsof variety, volume and velocity of data – both structuredand unstructured.
It is now widely used across industries, includingfinance, media and entertainment, government,healthcare, information services, retail, and otherindustries with big data requirements but the limitationsof the original storage infrastructure remain. Hadoop isincreasingly becoming the go-to framework for largescale, data-intensive deployments. Hadoop is built toprocess large amounts of data from terabytes to petabytesand beyond. With this much data, it’s unlikely
The Current Apache Hadoop ecosystem consists ofthe Hadoop Kernel, MapReduce, HDFS and numbers ofvarious components like Apache Hive, Base andZookeeper. HDFS and MapReduce are explained infollowing points.
A.Hadoop Distributed File System (HDFS)
Hadoop File System is the default file system on theservers in a Hadoop Cluster. It is developed usingdistributed file system design. It is run on commodity
automatic i.e. it needs human intervention. Hence,
Experts from various field have to design the model and
work to constantly improve it for better analysis.
It is a Data Warehouse Software built in Apache Hadoop for Querying and Managing Large Distributed
Apache Hiveis a component of Hortonworks Data Platform(HDP). Hiveprovides a SQL-like interface to data stored in HDP. Hiveprovides a database query
SOLUTION FOR BIG DATA PROCESSING-
Hadoop is a Programming framework used to support the processing of large data sets in a distributed
The need of Hadoop emerged with the avalanche of The World Wide Web was generating data at a
tremendous rate. The data generated had a lot of potential after analysis but the cost of doing so was also
The idea for Hadoop evolved in 2003 when Google published a paper named Google File System followed by MapReduce: Simplified Data Processing on Large
This inspired Doug Cutting and Mike Cafarella to create Hadoop, an open source framework called Hadoop which offered Big data processing on a Distributed
h a simple write-once storage infrastructure. It was initially developed as a filesystem which offered much faster data accession as well as stored multiple copies of the data providing a fail-safe storage solution. HDFS or Hadoop Distributed File
s a reliable storage infrastructure that stores the data in the form of blocks. These blocks are replicated on
Hadoop has moved far beyond its beginnings in web indexing and is now used in many industries for a huge variety of tasks that all share the common theme of lots
both structured
It is now widely used across industries, including finance, media and entertainment, government,
rvices, retail, and other industries with big data requirements but the limitations of the original storage infrastructure remain. Hadoop is
to framework for large intensive deployments. Hadoop is built to
arge amounts of data from terabytes to petabytes and beyond. With this much data, it’s unlikely
The Current Apache Hadoop ecosystem consists of the Hadoop Kernel, MapReduce, HDFS and numbers of various components like Apache Hive, Base and
and MapReduce are explained in
Hadoop Distributed File System (HDFS)12
Hadoop File System is the default file system on theservers in a Hadoop Cluster. It is developed using distributed file system design. It is run on commodity
hardware. Generally Distributed System are not designedto be Fault tolerant but HDFS is highly fault tolerant. Itstores multiple copies of data on low-helps in reducing the latency time as well as high dataavailability. The multiple copies helps HDFS recover thelost data in the event of a disk failure and ensures no datais lost. HDFS also makes applications available toparallel processing.
HDFS is designed using MASTERarchitecture. The Master node in this case is theNAMENODE and the slave node is the DATANODE
Fig2:Hadoop Master Slave Architecture
• Namenode
It is the manager of data on the entire HadoopCluster. Whenever a file is stored onto the HDFS, it issplit into blocks. The location of these blocks is managedby the Name Node. It also makes multiple copies of thedata for better reliability and availability. Since all theinformation is stored on namenode is very important,hence the Name Node is of High quality hardware and abackup of it is stored on a Network Drivefailure.
• Data Node
A Hadoop cluster comprises of several data nodes tostore the data. Each datanode is divided into blocks andeach block is allocated by the Name Node. Each blockcontains a part of the original file and no otherinformation about the other parts is available on the datanode.
B.Map-Reduce Architecture
Fig:3 Map reduce Architecture
MapReduce is the processing pillar in the Hadoopecosystem. It is mainly used for parallel processing oflarge sets of data stored in Hadoopframework an operation is applied on a huge data set,divide the problem and data and run it in parallel. Thiscan be done in multiple dimensions. For example a largedata set can be divided into smaller subsets where theoperation can be applied. In Hadoop, this is done bywriting MapReduce functions in java
hardware. Generally Distributed System are not designed to be Fault tolerant but HDFS is highly fault tolerant. It
-cost hardware. This helps in reducing the latency time as well as high data
copies helps HDFS recover the lost data in the event of a disk failure and ensures no data is lost. HDFS also makes applications available to
HDFS is designed using MASTER-SLAVE architecture. The Master node in this case is the
E and the slave node is the DATANODE
Fig2:Hadoop Master Slave Architecture
It is the manager of data on the entire HadoopCluster. Whenever a file is stored onto the HDFS, it is split into blocks. The location of these blocks is managed
ame Node. It also makes multiple copies of the data for better reliability and availability. Since all the information is stored on namenode is very important, hence the Name Node is of High quality hardware and a backup of it is stored on a Network Drive in the event of
A Hadoop cluster comprises of several data nodes tostore the data. Each datanode is divided into blocks and each block is allocated by the Name Node. Each block contains a part of the original file and no other
on about the other parts is available on the data
MapReduce is the processing pillar in the Hadoop ecosystem. It is mainly used for parallel processing of large sets of data stored in Hadoop cluster. In this framework an operation is applied on a huge data set, divide the problem and data and run it in parallel. This can be done in multiple dimensions. For example a large data set can be divided into smaller subsets where the
applied. In Hadoop, this is done by
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
51
These MapReduce program can be written easily using high level languages like Hive and Pig. The output of these programs is written back to either HDFS or in traditional data warehouse.
Two functions in MapReduce algorithms are as follows:
Map – This function takes some Key/value pairs as its input and generates an immediate output as key/value pairs.
Reduce – It is responsible for collaborating all the values with the same key together.
7. STOCK MARKET DATA ANALYSIS
There is high volume of data available from stock market trading. Every stock exchange manages the data whether it be NSE, BSE, NYSE, NASDAQ. The data obtained is completely unstructured. Hence, analysis of the data is difficult. The data needs to be transformed into meaningful format for analysis. Analysis of this data requires a lot of computational power and that too takes a lot of time.
The structured data obtained is loaded into the Hadoop Distributed File System. Once the data is loaded, the Hadoop File system makes copies of that data on different servers. This feature of HDFS provides high availability of data as well as Fault tolerance. If a single node fails, the backup copies are present.
The data is then mapped first using the map function and then reduced using the reduce function.
Alternatively, Apache PIG can do this job.
The Hadoop cluster can also be used for static analysis of data. The data set can be imported into the HDFS and processed.
Hence, the analytics of historic data can be done as well. Hadoop also supports live streaming of data for real-time analysis.
REFERENCES
1. Sulochana Panigrahi and S Mohan Kumar, “A survey onsocial data processing using apache Hadoop, Map-Reduce,” International Journal of Scientific and TechnicalAdvancements, Volume 2, Issue 2, pp. 121-123, 2016
2. Harshawardhan S. Bhosale , Prof. Devendra P. Gadekar,”A Review Paper on Big Data and Hadoop“,”InternationalJournal of Scientific and Research Publications, Volume 4,Issue 10, October 2014”
3 .Rahul Beakta,”Big Data And Hadoop: A Review Paper”,”Recent Innovation in Electronics, Electrical & Computer Science Engineering – 2015”
4. Ashwini A. Pandagale & Anil R. Surve,”Big Data AnalysisUsing Hadoop Framework”,”International Journal ofResearch and Analytical Reviews 2016”
5. Harin C Naik, Divyesh Joshi,”A Hadoop FrameworkRequire to Process Bigdata very Easily andEfficiently”,”International Journal of Scientific Researchin Science, Engineering and Technology-2016”
6 .Apache Hadoop at https://hortonworks.com/apache/hadoop/
7. Veereshetty Dagade, Abhishek Akkatangerhal, AmrutaDeshpande, Rucha Bhandiwad,”Big Data Stock Analysisusing Hadoop”,”International Journal of EmergingTechnology in Computer Science and Electronics- April2015”
8. Big Data from https://en.wikipedia.org/wiki/Big_data
9. 3Vs definition from http://whatis.techtarget.com/definition/3Vs
10. http://searchcloudcomputing.techtarget.com/definition/big-data-Big-Data
11. The Challenges of Block Chain Indexing fromhttps://medium.com/@lopp/the-challenges-of-block-chain-indexing-30527cf4bfbd
12. HDFS from https://www.dezyre.com/article/hadoop-components-and-architecture-big-data-and-hadoop-training/114
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
52
Generation of Business Intelligence by
Sentimental Analysis through Big Data and
Hadoop 1Abhinav Juneja,
2Prayans Jain,
3Siddharth
1Associate Professor, Department of CSE, BMIET, Sonepat
2,3B.Tech, CSE, 4
th Year, BMIET, Sonepat
Abstract--Online networking gives clients a platform
to discuss successfully with companions, family, and
partners, and gives them a stage to discuss their top
pick (and minimum most loved brands). This
"unstructured" discussion can provide organizations
crucial understanding into how buyers see their image,
and enable them to effectively settle on business choices
to keep up their horizons. Fast in the volume of
conclusion rich online networking on the web has
brought about an expanded enthusiasm among
specialists with respect to Sentimental Analysis and
Opinion Mining. In any case, with so much web-based
social networking accessible on the web, Sentiment
Analysis is presently considered as a Big Data
assignment. The concentration of the examination was
to discover such a system, to the point that can
productively perform Sentiment Analysis on Big Data
sets. In this paper Sentiment Analysis was performed
on an extensive informational index of tweets utilizing
Hadoop and the execution of the strategy was
measured in type of speed and precision. The test result
demonstrates that the procedure displays great
effectiveness in taking care of huge feeling
informational collections. Today in the era of cloud and
matrix involving the incorporation of information from
heterogeneous databases is unavoidable. This will end
up plainly complex when size of the database is
exceptionally tremendous. MapReduce is another
system particularly actualized for preparing vast
datasets on conveyed sources. Hadoop has inner
complex structure like MapReduce to execute the faster
execution on inquiry and provides the quick outcome.
To improve the execution, we are utilizing Hadoop
stage which has ability to deal with Big data.
I. INTRODUCTION
Big Data is upcoming area of research in Computer Science and, Sentiment Analysis is one of the most important component of this research area. Big Data is considered as very large amount of data which can be found easily on web, Social media networks, remote sensing data and medical services records etc. in form of structured, semi-structured or
unstructured data and we can utilize this data for Sentiment Analysis.
Sentimental Analysis is all about to get the real voice of people towards specific product, services, organization, movies, news, events, issues and their attributes[1]. By using approaches, methods, techniques and models of defined branches, we can categorize our unstructured data which may be in the form of news articles, blogs, tweets, movie reviews, product reviews etc. into positive, negative or neutral sentiment according to the sentiment expressed in them.
A. Level of sentiment
1) Attribute-Level Analysis
Attribute level analysis provides a sentiment foreach object in a sentence .This behavior is the default level of analysis for Pulse. Attribute analysis identifies the objects of a sentence and any sentiment expressed regarding those objects.
2) Sentence -Level Analysis
A sentence level analysis provides the overallsentiment of each sentence in a document. If a sentence is contains both positive and negative sentiments, it appears as mixed [1].
3) Document -Level Analysis
Document level analysis provides the overallsentiment of an entire document. If you wanted to know if a movie review was positive, negative, or mixed, a document level analysis could provide that information. Document level analysis gives both the overall sentiment score and a mixed rating if the sentiment is not exclusively positive or negative [1].
II. RELATED WORKS
A. Hadoop
Apache’s Hadoop is an implementation of Map Reduce. Hadoop has been applied successfully for
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
53
file based datasets. The Apache Hadoop project develop open-source software for reliable distributed computing system. Existing tools are not designed to handle such large amount of data. Hadoop avoids the drawbacks by effectively storing and providing computational capabilities over substantial amounts of data.
B. Map Reduce
Hadoop MapReduce (Hadoop Map/Reduce) is a software application framework for the distributed processing of large data sets on compute clusters of commodity hardware. It is a sub-project of the Apache Hadoop project. The framework takes care of scheduling tasks, monitoring them and re-executing of any failed tasks.
According to The Apache Software Foundation, the primary objective of Map/Reduce is to split the input data set into independent blocks that are processed in a dominanantly parallel manner. The Hadoop MapReduce framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically, both the input and the output of the job are stored in a file system.
Map: The master node takes the input and divides it into smaller sub problems and distributes them to worker nodes. A worker node may consider to do this repeatedly, leading to a multi-level tree structure. The worker nodes processes the small problems and sends the results back to its master node [2].
Reduce: The master node in turn collects the results to all the sub problems and integrates them in some way to form the final output – the result to the problem it was originally trying to solve. As the various tasks are run in parallell, it manages all communications and data transfers between the various parts of the system [2].
C. Sentimental analysis
Sentiment analysis (sometimes known as opinion mining or emotion AI) refers to the utilization of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.
Generally speaking, sentiment analysis is applied to determine the attitude of a speaker, writer, or other subject with respect to some topic or the overall
polarity contextually or emotional reaction to some document, interaction, or event. The attitude may be a judgment or evaluation (see appraisal theory), affective state (that is to say, the emotional state of the author or speaker), or the intended emotional communication (that is to say, the emotional effect intended by the author or interlocutor).
III. EXISTING METHOD
Vocabulary Based systems take a shot at a presumption that the aggregate extremity of a report or sentence is the entirety of polarities of the individual words or expressions. A portion of the noteworthy works done utilizing this system are:
Kamps [3] utilized a basic system in view of lexical relations to perform grouping of content.
Andrea [4] utilized word net to characterize the content utilizing a suspicion that words with comparative extremity have comparative introduction.
Ting-Chun [5] utilized a calculation in view of pos (grammatical feature) patter. A content expression was utilized as a question for a web crawler and the outcomes were utilized to characterize the content.
Prabhu [6] which utilized a straightforward vocabulary based method to remove opinions from twitter information.
Turney [7] utilized semantic introduction on client surveys to distinguish the fundamental estimations.
Taboada [8] utilized dictionary based way to deal with extricate estimations from smaller scale web journals.
Assumption examination for smaller scale sites is all the more difficult as a result of issues like utilization of short length status message, casual words, word shortening, spelling variety and emojis. Twitter information was utilized for sentimental analysis [9].
Negative word can switch the extremity of any sentence. Taboada performed notion investigation while taking care of nullification and elaborating words. Part of refutation was reviewed. Minquing [10]. grouped the content utilizing a basic dictionary based approach with highlight identification. It was watched that a large portion of these current strategies doesn't scale to huge informational collections effectively. While different machine learning systems shows preferable exactness over vocabulary based methods, they take additional time in preparing the calculation and henceforth are not reasonable for huge informational indexes. In this
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
54
paper, dictionary based approach is utilized to groupthe content as per extremity.
IV. PROPOSED WORK
A. System Architecture
In the system Architecture it shows thesentimental analysis of the selected product using theBig Data [11]. We are taking the Twitterwhich is huge data and increasing day by day. Forhandling this big data, we used Hadoop platformwhich is developed by Java and which have internalframework like Map Reduce [12]. For Hadoop werequired the Linux operating system hence we arecreated virtual machine. Twitter Data is passed to theHadoop core (VMware) by using SCP tool. Hadooptakes the twitter data which processed it and generatethe structure data. By analyzing the sentiment whichcustomer is posted on twitter like good feedbackbad feedback. After analyzing the data we are goingto show feedback system is going to display ingraphical format.
Fig1. System Architecture
B. Proposed Work
We are developing a system that analyzessentiment posted on twitter. Like positive tweets ornegative tweets. The sentimental analysis is doneusing tweets on twitter. Also, the system is going tohandle big data which will be continuouslyincreasing and our system will analyze based on treal-time data. To optimize the performance, we areusing Hadoop platform which has capability tohandle the big data. After analyze the data we aregoing to show sentiment
dictionary based approach is utilized to group
PROPOSED WORK
In the system Architecture it shows the sentimental analysis of the selected product using the
. We are taking the Twitter Database which is huge data and increasing day by day. For handling this big data, we used Hadoop platform which is developed by Java and which have internal
. For Hadoop we required the Linux operating system hence we are
ated virtual machine. Twitter Data is passed to the Hadoop core (VMware) by using SCP tool. Hadoop takes the twitter data which processed it and generate the structure data. By analyzing the sentiment which customer is posted on twitter like good feedback or bad feedback. After analyzing the data we are going to show feedback system is going to display in
a system that analyzes the sentiment posted on twitter. Like positive tweets or negative tweets. The sentimental analysis is done using tweets on twitter. Also, the system is going to handle big data which will be continuously increasing and our system will analyze based on the
time data. To optimize the performance, we are using Hadoop platform which has capability to handle the big data. After analyze the data we are
C. Proposed Procedure
The focus of this project was to discover anapproach that can perform Sentiment Analysis on thegrounds that huge volume of information shouldhave been harvested. Likewise, it must be ensuredthat exactness isn't traded off excessively whileconcentrating on speed of decision making.Prediction Analysis on Big Data is accomplished byworking together on Big Data with Hadoop
The proposed approach is a word reference basedstrategy i.e. a lexicon of slant bearing words wasutilized to group the content into positive, negativeor nonpartisan conclusion. Machine learningtechniques [13] are not utilized in light of the factthat in spite of the fact that they are more exact thanthe word reference based methodologies, they take toan extreme degree an excessive amount of timeperforming Sentiment Analysis as they must beprepared first and thus are not effective in takingcare of huge supposition information.
1. Real Time Data and Features:
• Length
The maximum length of a tweet is about 140characters. This is very different from the previoussentiment classification research that focused onclassifying longer bodies of work, such asreviews.
• Data Availability
Another difference is the magnitude of dataavailable. With the Twitter API, twitter4j [14very easy to collect millions of tweets for trainingwhich allows the developer an access to 1% oftweets tweeted at that time basis on the particularkeyword.
• Language Model
Twitter users post messages from many differentmedia, including their cell phones. The frequency ofmisspellings and slang in tweets is much higher thanin other domains.
• Domain
Twitter users post short messages about a varietyof topics unlike other sites which are tailored to aspecific topic. This differs from a large percentage ofpast research, which focused on specific domainssuch as movie reviews.
D. Sentimental Dictionary
The dictionary contains all forms of a word i.e.every word is stored along with its various verbforms e.g. applause, applauding, applauded,applauds. Hence eliminating the need for stemming
The focus of this project was to discover an an perform Sentiment Analysis on the
grounds that huge volume of information should have been harvested. Likewise, it must be ensured that exactness isn't traded off excessively while concentrating on speed of decision making.
ta is accomplished by Big Data with Hadoop
The proposed approach is a word reference based strategy i.e. a lexicon of slant bearing words was utilized to group the content into positive, negative or nonpartisan conclusion. Machine learning
are not utilized in light of the fact in spite of the fact that they are more exact than
the word reference based methodologies, they take to an extreme degree an excessive amount of time performing Sentiment Analysis as they must be prepared first and thus are not effective in taking
huge supposition information.
Real Time Data and Features:
The maximum length of a tweet is about 140 characters. This is very different from the previous sentiment classification research that focused on classifying longer bodies of work, such as movie
Another difference is the magnitude of data h the Twitter API, twitter4j [14], it is
very easy to collect millions of tweets for training which allows the developer an access to 1% of
me basis on the particular
Twitter users post messages from many different media, including their cell phones. The frequency of misspellings and slang in tweets is much higher than
messages about a variety of topics unlike other sites which are tailored to a specific topic. This differs from a large percentage of past research, which focused on specific domains
The dictionary contains all forms of a word i.e. every word is stored along with its various verb forms e.g. applause, applauding, applauded, applauds. Hence eliminating the need for stemming
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
55
each word which saves more time. The also contains the strength of the polarity Dictionary of every word. Some word depicts stronger emotions than others. For example, good and great are both positive words but great depict a much stronger emotion.
E. Handling Invalidation AND Daze nullification
(Negation and Blind Negation)
Invalidation words are the words which turn around the extremity of the feeling associated with the content. For instance, „the film was not good‟. Despite the fact that the word „good‟ delineates a positive opinion the invalidation – „not‟ turns around its extremity. In the proposed approach at whatever point a negation word is experienced in a tweet, its polarity is reversed [13,15,16,].
Daze nullification words are the words which work on the sentence level and calls attention to a component that is wanted in an item or administration. For instance, in the sentence „the acting should have been better‟, „better‟ delineates a positive assumption however the nearness of the visually impaired invalidation word-„needed ‟suggests that this sentence is really portraying negative notion. In the proposed approach at whatever point a visually impaired invalidation word happens in a sentence its extremity is instantly named as negative.
V ALGORITHM
Algorithm: ALGO_SENTICAL
Input: Tweets, SentiWord_Dictionary
Output: Sentiment (positive, negative or neutral)
BEGIN
1)For each tweet Tido the following
2)Initialize SentiScore = 0;
3)For each word Wj in Ti that exists in
Sentiword_Dictionary.
If polarity[Wj] = blind negation then Return
negative.
Else
If polarity[Wj] = “acceptable ” then increment
seniscore by 1.
Else If polarity [Wj] = “average OR good” then add
2 to sentiscore.
if polarity [Wj] = “better OR best” then increment
sentiscore by 3.
Else If polarity [Wj] = “Excellent” then add 4 to
sentiscore.
Else If polarity [Wj] = “below average ” then
decrement sentiscore by 1.
Else If polarity [Wj] = “bad “then subtract 2 from
sentiscore.
Else If polarity[Wj] = “worse ” then decrement
sentiscore by 3.
Else If polarity[Wj] = “not acceptable “then subtract
4 from sentiscore.
If Sentiscore of Ti>0 then Sentiment = positive.
Else If Sentiscore of Ti<0 then Sentiment = negative.
Else Sentiment = neutral
4)Return Sentiment
5)END
Fig. 2 Different Categories of Expressions
V. SCOPE OF THE SYSTEM
The current technique involves sentiments of the user which is plain text format like tweet from twitter and it is on big data which is continuously increasing.
For performance of the system we are using Hadoop platform which is going to handle big data.
This system is useful to improve the quality of the product and customer satisfaction which will be useful for business growth.
VI. CONCLUSION AND FUTURE
WORK
Sentimental Analysis is being utilized for various applications and can be utilized for a few others in future. It is clear that its applications will grow to more zones and will keep on encouraging increasingly exploration in the field. In this project work, fundamental concentration was on performing Sentiment Analysis rapidly with the goal that Big Data sets can be taken care of effectively. The work can be additionally extended by presenting procedures that expand the exactness by handling issues like non conventional expressions, articulations and certain assumptions which still should be settled appropriately. Additionally, as clarified prior, this work is being actualized on a solitary hub arrangement and despite the fact that it is normal that it will perform much better in a
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
56
multimode venture level setup, it is attractive to check its execution in such condition in future.
REFERENCES
[1] Bing Liu, Sentiment Analysis and Opinion Mining, Morgan
and Claypool Publishers, May 2012.p.18-19,27-28,44-45,47,90-
101.
[2] D.Gillick, A.Faria, and J.DeNero, “MapReduce”:
DistributedComputing for Machine Learning”, IEEE Transaction,
Dec 2006 .
[3] Kamps, Maarten Marx, Robert J. Mokken and Maarten De
Rijke, “Using wordnet to measure semantic orientation of
adjectives”, Proceedings of 4th International Conference on
Language Resources and Evaluation, pp. 1115-1118, Lisbon,
Portugal, 2004.
[4] Andrea Esuli and Fabrizio Sebastiani, “Determining the
semantic orientation of terms through gloss classification”,
Proceedings of 14th ACM International Conference on
Information and Knowledge Management,pp. 617-624, Bremen,
Germany, 2005.
[5] Ting-Chun Peng and Chia-Chun Shih , “An Unsupervised
Snippet-based Sentiment Classification Method for Chinese
Unknown Phrases without using Reference Word Pairs”, 2010
IEEE/WIC/ACM International Conference on Web Intelligence
and intelligent Agent Technology JOURNAL OF COMPUTING,
VOLUME 2, ISSUE 8, AUGUST 2010, ISSN 2151-9617 .
[6] Prabu Palanisamy, Vineet Yadav, Harsha Elchuri, “Serendio:
Simple and Practical lexicon based approach to Sentiment
Analysis”, Serendio Software Pvt Ltd, 2013.
[7] Peter Turney and Michael Littman. 2003. Measuring praise
and criticism: Inference of semantic orientation from association.
ACM Transactions on Information Systems 21(4):315–346.
[8]Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll
and Manfred Stede. 2011. Lexicon-based methods for sentiment
analysis. Computational linguistics, volume 37, number2, 267–
307, MIT Press.
[9] Albert Bifet and Eibe Frank. 2010. Sentiment knowledge
discovery in twitter streaming data, Discovery Science 1–14,
Springer.
[10] Minqing Hu, Bing Liu. Mining and Summarizing Customer
Reviews, Department of Computer Science, University of Illinois
at Chicago, Research Track Paper.
[11] Xindong Wu, Fellow,Xingquan Zhu, Gong-Qing Wu, and
Wei Ding ”Data Mining with Big Data”, IEEE Transaction,
JANUARY 2014.
[12] ].Ralf Lammel. Google's MapReduce Programming Model
Revisited.Science of Computer Programming archive. Volume 68,
(2008).
[13] Long-Sheng Chen, Cheng-Hsiang Liu, Hui -Ju Chiu, “A
neural network based approach for sentiment classification in the
blogosphere”, Journal of Informetrics 5 (2011) 313–322.
[14] http://twitter4j.org/en/index.html.
[15] Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly
Voll and Manfred Stede. 2011. Lexicon based methods for
Sentiment Analysis. Computational linguistics, volume 37,
number2, 267–307, MIT Press. .
[16] Michael Wiegand, Alexandra Balahur, Benjamin Roth,
Dietrich Klakow, Andr´es Montoyo. 2010. Asurvey on the role of
negation in Sentiment Analysis. Proceedings of the workshop on
negation speculation in natural language processing 60–68,
Association for Computational Linguistics.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
57
Dynamic Update and Public Auditing with Dispute
Arbitration for Cloud Data
Dr.B.Mahesh
Associate Professor, Department of CSE, Malla Reddy Engineering College and Management Sciences
Medchal, Telangana, India
Abstract—Cloud computing is a kind of computing that relies
on allocation computing resources rather than having local
servers or personal devices to handle applications. Storage
outsourcing became a upward trend with the arrival of the cloud
computing promoting the secure remote data auditing to be
appeared in the explore. Moreover this explore considers the
problem of data dynamics support, public verifiability and
dispute arbitration concurrently. The data dynamics problem in
auditing is solved by introducing an index switcher to keep a
mapping amid block indices and tag indices, and purge the
passive effect of block indices in tag computation lacking
incurring much overhead. We provide equality guarantee and
dispute arbitration in our scheme, which ensures that both the
data owner and the cloud cannot act up in the auditing process or
else it is easy for a third-party arbitrator to find out the devious
party. The system is extended by implementing the data
dynamics and fair arbitration on groups in future.
Keywords—Integrity auditing, public verifiability, dynamic
update, arbitration, fairness.
I. INTRODUCTION
Data outsourcing is a key application of cloud computing, which relieves cloud users of the heavy burden of data management and infrastructure maintenance, and provides fast data access autonomous of physical locations. However, outsourcing data to the cloud brings about lots of new security threats. Firstly, regardless of the powerful machines and well-built security mechanisms provided by cloud service providers (CSP), secluded data still face network attacks, hardware failures and administrative errors. Secondly, CSP may regain storage of rarely or never accessed data, or even hide data loss accidents for standing reasons. As users no longer physically hold their data and consequently lose direct control over the data, direct employment of traditional cryptographic primitives like hash or encryption to make sure remote data’s integrity may lead to many security loopholes. In particular, downloading all the data to check its integrity is not feasible due to the classy communication overhead, especially for large-size data files. In this sense, message authentication code (MAC) or signature based mechanisms, while extensively used in secure storage systems, are not suitable for integrity check of outsourced data, because they can only verify the integrity of retrieved data and do not work for infrequently accessed data (e.g., archive data). So how to ensure the rightness of outsourced data without possessing the original
data becomes a challenging task in cloud computing, which, if not effectively handled, will impede the wide deployment of cloud services.
Data auditing schemes can facilitate cloud users to check the integrity of their remotely stored data without downloading them locally, which is termed as blockless verification. With auditing schemes, users can periodically interact with the CSP through auditing protocols to check the accuracy of their
outsourced data by verifying the integrity proof computed by the CSP, which offers stronger assurance in data security because user’s own conclusion that data is integral is much more persuasive than that from service providers. Generally speaking, there are several trends in the development of auditing schemes. First of all, earlier auditing schemes usually require the CSP to generate a deterministic proof by accessing the whole data file to perform integrity check, e.g., schemes in [1], [2] use the entire file to execute modular exponentiations. Such plain solutions gain expensive computation overhead at the server side, hence they lack efficiency and practicality when dealing with large-size data. Represented by the ”sampling” method in ”Proofs of Retrievability” (PoR) [3] model and”Provable Data Possession” (PDP) [4] model, later schemes [5], [6] tend to present a probabilistic proof by accessing branch of the file, which clearly enhances the auditing efficiency over past schemes. Secondly, a few auditing schemes [3], [7] offer private verifiability that need only the data owner who has the private key to do the auditing task, which may potentially overload the owner due to its limited computation capability. Ateniese el al. [4] was the first to suggest to enable public verifiability in auditing schemes. In disparity, public auditing schemes [5], [6] allow anyone who has the public key to perform the auditing, which makes it possible for the auditing task to be delegated to an external third party auditor (TPA).
A TPA can perform the veracity check on behalf of the data owner and truthfully report the auditing result to him [8]. Thirdly, PDP [4] and PoR [3] mean to audit static data that are hardly ever updated, so these schemes do not supply data dynamics support. But from a universal viewpoint, data update is a very common requirement for cloud applications. If auditing schemes could only deal with static data, their viability and scalability will be limited. On the other hand, direct extensions of these static data oriented schemes to
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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support dynamic update may source other security threats, as explained in [6]. To our knowledge, only schemes in [6], [9], [10] provide built-in support for fully data dynamic operations(i.e., amendment, addition and removal), but they are deficientin providing data dynamics support, public verifiability andauditing efficiency simultaneously, as will be analyzed in thesection of related work. From these trends, it can be seen thatgiven that probabilistic proof, public verifiability and datadynamics support are three most crucial characteristics inauditing schemes. Among them, providing data dynamicssupport is the most challenging. This is because nearly allexisting auditing schemes intend to implant a block’s indexinto its tag computation, e.g., H(i||v) in [4] or H(name||i) in [5],which serves to authenticate challenged blocks. Conversely, ifwe insert or delete a block, block indices of all subsequentblocks will change, then tags of these blocks have to be re-computed. This is unacceptable because of its highcomputation overhead. We tackle this problem bydifferentiating among tag index (used for tag computation)and block index (indicate block position), and rely an indexswitcher to keep a mapping between them. Upon each updateoperation, we assign a new tag index for the operating blockand update the mapping connecting tag indices and blockindices. Such a layer of indirection among block indices Bandtag indices enforces block authentication and avoids tag re-computation of blocks after the operation positionsimultaneously. As a result, the efficiency of handling datadynamics is greatly enhanced. Furthermore and important, in apublic auditing scenario, a data owner always delegates hisauditing tasks to a TPA who is trusted by the owner but notbasically by the cloud. Current explore usually assumes antruthful data owner in their security models, which has aninborn inclination toward cloud users. However, the fact is,not only the cloud, but also cloud users, have the motive toconnect in deceitful behaviors. For example, a malicious dataowner may intentionally claim data corruption adjacent to anhonest cloud for a money reimbursement, and a dishonest CSPmay delete rarely accessed data to save storage. Therefore, itis of serious importance for an auditing scheme to providesprite guarantee to settle potential disputes between the twoparties. Zheng et al. planned a fair PoR scheme to avoid adishonest client from reproving an honest CSP, but theirscheme only realizes private auditing. Kupccu [12] plannedgeneral arbitration protocols with automated costs using fairsignature exchange protocols [13]. Our stab also adopts theidea of signature exchange to make sure the metadata accuracyand protocol fairness, and we concentrate on combiningefficient data dynamics support and fair dispute arbitrationinto a single auditing scheme. To address the fairness problemin auditing, we introduce a thirdparty arbitrator(TPAR) intoour threat model, which is a specialized institute for conflictsarbitration and is trusted and payed by together data ownersand the CSP. Since a TPA can be viewed as a delegator of thedata owner and is not unavoidably trusted by the CSP, wedistinguish among the roles of auditor and arbitrator.Moreover, we implement the idea of signature exchange toensure metadata correctness and offer dispute arbitration,where any conflict about auditing or data update can be fairlyarbitrated.
II. DYNAMIC AUDITING SCHEME
Let G1, G2 and GT be multiplicative cyclic groups of prime
order p, g1 and g2 be generators of G1 and G2, respectively. Let
e : G1 × G2 → GT be a bilinear map, and H(·) : 0, 1 ∗ → G1
be a secure public map-to-point hash function, which maps a
string 0, 1 ∗ uniformly into an element of G1. Let Sigsk(seq,
Ω) ← (h(seq||Ω))sk
denote a signature on the concatenation of
a sequence number seq and the index switcher Ω using the
private key sk. Let skc and sks denote the private key of the
client and the CSP, respectively. Then the scheme can be
described as follows.
KeyGen. The data owner randomly chooses α ← Zp and u ←
G1, computes v ← g α and w ← u α. The secret key is sk = α
and the public key is pk = (v, w, g, u).
TagGen. Given a data file F = m1, m2, . . . , mn. For each
block mi , the owner computes its tag as σi = (H(ti) · umi
) α,
where ti denotes the tag index of the block. Denote the tag set
by Φ = σi1≤i≤n. Initially, tag indices and block indices are
the same sequence 1, 2, . . . , n, so tag computation can be
simplified as σi = (H(i)·umi
) α, and the TPAR can easily
construct his version of the index switcher. Then, the owner
computes his signature on the index switcher Sigc = Sigskc
(seq0, Ω0), where seq0 is initialized to 0 and Ω0 = (i, ti =
i)1≤i≤n. Finally, the owner sends F, Φ, Sigc to the CSP for
storage and sends pk to the TPAR. The owner deletes its local
copy of F, Φ and keeps the index switcher Ω.
Commitment. This procedure is to avoid a malevolent owner
from generating incorrect tags at the initial stage so that he can
falsely accuse the cloud at a later time. The cloud generates
deterministic proof from all received blocks and tags
according to algorithm ProofGen and verify its validity with
algorithm ProofVerify. If the substantiation succeeds, the
cloud can be convinced that all tags are correctly computed
from received blocks, and then he sends his signature on the
index switcher Sigs = Sigsks (seq0, Ω0) to the client for storage,
where seq0 = 0 and Ω0 = (i, ti = i)1≤i≤n. The client also
verifies the correctness of Sigs, if succeeds, he keeps it;
otherwise he contacts the TPAR for arbitration.
III. SCHEME DESCRIPTION
In presented public auditing schemes [4], [5], [6], [14] mainly focus on the assignment of auditing tasks to a third party auditor (TPA) so that the transparency on clients can be offloaded as much as possible. However, such models have not critically considered the fairness problem as they usually assume an truthful owner against an untrusted CSP. Since the TPA acts on behalf of the owner, then to what level could the CSP trust the auditing result? What if the owner and TPA get together together against an honest CSP for a financial compensation? In this sense, such models diminish the practicality and applicability of auditing schemes. In a cloud scenario, both owners and CSP have the reason to cheat. The CSP makes turnover by selling its storage capacity to cloud users, so he has the motive to recover sold storage by deleting rarely or not at all accessed data, and even hides data loss accidents to maintain a reputation. Here, we assume the CSP
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
59
is semi-trusted, namely, the CSP behaves appropriately as prescribed contract most of the time, but he may try to pass the truthfulness check without possessing correct data. On the other hand, the owner also has the motive to wrongly accuse an honest CSP, e.g., a malicious owner intentionally claims data corruption despite the fact to the contrary so that he can get a compensation from the CSP. Therefore, disputes between the two parties are inescapable to a certain degree. So an arbitrator for dispute decision is indispensable for a fair auditing scheme. We extend the threat model in accessible public schemes by differentiating the auditor (TPAU) and the arbitrator (TPAR) and putting dissimilar trust assumptions on them. Because the TPAU is mainly a delegated party to check client’s data integrity, and the potential dispute may occur between the TPAU and the CSP, so the arbitrator should be an impartial third party who is different to the TPAU. As for the TPAR, we consider it honest-but-curious. It will behave truthfully most of the time but it is also inquisitive about the content of the auditing data, thus the privacy protection of the auditing data should be measured.
Figure 1: System Architecture
As illustrated in Fig. 1, the system model involves five
different entities:
1. The data owner, who has a huge amount of data to be stored
in the cloud, and can dynamically update his data (e.g., add,
remove or adjust a data block) in the future.
2. The cloud service provider (CSP), who has immense
storage space and computing power that users do not hold,
stores and manages user’s data and related metadata.
3. The third party auditor (TPAU) is a public verifier with
information and capabilities for auditing, and is trusted and
payer by the data owner (but not necessarily trusted by the
CSP) to validate the integrity of the owner’s remotely stored
data.
4. The third party arbitrator (TPAR), is an entity for
impending conflict arbitration and trusted by both the owner
and the CSP, and is different to the role of TPAU.
5. Users, who can seek and download multiple files at a time
from the cloud with the owner’s permission in secure fashion.
Only the files activated by the TPAR can be available to
download. Data Owner rely on the CSP for data storage and
protection, and they may access and renew their data. To
assuage their burden, cloud users can delegate auditing tasks
to the TPAU, who periodically performs the auditing and
truthfully reports the result to users. For potential disputes
between the data owner, auditor and the CSP, the TPAR can
fairly settle the disputes on proof verification or data update.
IV. EXPERMENTAL RESULTS
The volume of the blocks of the data file is equal. For
example, if the volume of the test data is 9 KB, then it is
divided into 3 block volume of fragmentation each of volume
3 KB.. I calculate the performance of the auditing scheme
from three aspects: tag/token generation time, proof invention
time and proof confirmation time. For data dynamic update
and dispute arbitration, we test the update in the clouds by
adding, removing and renewing some blocks.
Figure 2: Cost of Blocks
For data dynamics, I experiment the in the clouds of adding,
removing and renewing 1 block and corresponding tag, as
illustrated in Fig 2. The graph in Fig 3 shows how the future
scheme search algorithm searches many files in the same time
when compared to presented schemes where one file can be
searched at a time. Since it takes fewer time to search and
hence its performance is better.
Figure 3: Search File Performance
V. CONCLUSION
In this paper we revise the need of a fair and dynamic auditing
scheme to avert a lying client reproving an honest CSP. But
their scheme only realizes private auditing, and is hard to be
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
60
extended to support public auditing. Compared to these
schemes, here is we join public verifiability, data dynamics
support and dispute arbitration simultaneously. The system is
extensive by implementing the data dynamics andfair
arbitration on groups in future.
REFERENCES
[1] Y. Deswarte, J.-J.Quisquater, and A. Sa ıdane, “Remote integrity checking,” in Proc. 5th Working Conf. Integrity and Intl Control in Information Systems, 2004, pp. 1–11.
[2] D.L. GazzoniFilho and P.S.L. M. Barreto, “Demonstrating data possession and uncheatable data transfer.” IACR Cryptology ePrint Archive, Report 2006/150, 2006.
[3] A. Juels and B. S. KaliskiJr, “Pors: Proofs of retrievability for large files,”in Proc. 14th ACM Conf. Computer and Comm. Security (CCS07), 2007, pp. 584–597.
[4] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L.Kissner, Z. Peterson, and D. Song, “Provable data possession at untrusted stores,” in Proc. 14th ACM Conf. Computer and Comm. Security (CCS07), 2007, pp. 598–609.
[5] H.Shacham and B. Waters, “Compact proofs of retrievability,” in Proc. 14th Intl Conf. Theory and Application of Cryptology and Information Security: Advances in Cryptology (ASIACRYPT 08), 2008, pp. 90–107.
[6] Q. Wang, C. Wang, J. Li, K. Ren, and W. Lou, “Enabling public verifiability and data dynamics for storage security in cloud computing,” in Proc. 14th European Conf. Research in Computer Security (ESORICS 08), 2009, pp. 355–370.
[7] M. A. Shah, R. Swaminathan, and M. Baker, “Privacypreserving audit and extraction of digital contents.” IACR Cryptology ePrint Archive, Report 2008/186, 2008.
[8] C. Wang, K. Ren, W. Lou, and J. Li, “Toward publicly auditable securecloud data storage services,” Network, IEEE, vol. 24, no. 4, pp. 19–24, 2010.
[9] C. Erway, A. K¨upc ¨ u, C. Papamanthou, and R.Tamassia, “Dynamic provable data possession,” in Proc. 16th ACM Conf. Computer and Comm. Security (CCS 09), 2009, pp. 213–222.
[10] Y. Zhu, H.Wang, Z. Hu, G.-J.Ahn, H. Hu, and S. S. Yau, “Dynamic audit services for integrity verification of outsourced storages in clouds,” in Proc. ACM Symp. Applied Computing (SAC 11), 2011, pp. 1550–1557.
[11] Q. Zheng and S. Xu, “Fair and dynamic proofs of retrievability,” in Proc. 1st ACM Conf. Data and Application Security and Privacy (CODASPY 11), 2011, pp. 237–248.
[12] A. K¨upc ¨ u, “Official arbitration with secure cloud storageapplication,” The Computer Journal, pp. 138–169,2013.
[13] N. Asokan, V. Shoup, and M. Waidner, “Optimistic fair exchange ofdigital signatures,” in Proc. 17th Intl Conf. Theory and Applications ofCryptographic Techniques:Advances in Cryptology (EUROCRYPT98), 1998,pp.591–606.
[14] C.Wang, Q.Wang, K. Ren, andW. Lou, “Privacypreserving publicauditing for data storage security in cloud computing,” in Proc. IEEEINFOCOM, 2010, pp. 1–9.
[15] C. Wang, S. S. Chow, Q. Wang, K. Ren, and W. Lou,“Privacy preserving public auditing for secure cloud storage,” IEEE Trans. Computers, vol. 62, no. 2, pp. 362–375, 2013.
[16] B. Wang, B. Li, and H. Li, “Oruta: Privacy-preserving public auditing for shared data in the cloud,” IEEE Trans. Cloud Computing, vol. 2, no. 1, pp. 43–56, 2014.
[17] “Proofs of retrievability with public verifiability and constant communication cost in cloud”, J.Yuan and S.Yu,International workshop on security in cloud computing, may 2013 .
[18] “Provable Multicopy Dynamic Data Possession in Cloud Computing Systems”,Ayad F. Barsoum and M. Anwar Hasan, IEEE Transactions On Information Forensics And Security, march 2015.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
61
A Survey on Big Data Storage Issues in Cloud
Computing Environment Ashima Arya
1 Research Scholar, Department of Computer Engineering Chitkara University,Punjab.
Jagpreet Sidhu2 ,Associate ProfessorDepartment of Computer Engineering Chitkara University,Punjab.
Abstract- Big data is the collection of large set of data
which holds many intelligence and raw information. In the
digital world, handling and updating large volume of data
in the real time environment is a challenging task. This
paper presents a compressive discussion on analytical
techniques and processing methods for analysing the
applications of big data with the purpose of constructing
valuable information in cloud computing environment.
This research aims to evaluate the existing research and
discusses on security issues associated with big data in
cloud computing.
Keywords--Big data, Cloud Computing, Analytics,
Security.
1. INTRODUCTION
Big Data
The term big data is defined as “a new generation of technologies, architectures and frameworks that are designed to economically separate valuable information from very large variety of data, by enabling high velocity capture, discovery, extraction and analysis” [1]. In the digital era, large amount of data have been generated from various sources such as social network, internet of things, scientific experiments, healthcare applications etc. at a rapid speed.
According to the renowned IT companies, the total amount of data in the world has increased nine times within five years [2]
.The big data is explained by 3Vs as Volume, Velocity, Variety .The term volume refers to the size of the data, velocity refers to the speed of incoming and outgoing data, and variety describes the sources and types of data .To define the big data efficiently more factors such as veracity, value, variability, validity and vagueness are added to some complementary explanation of data.
Traditional methods of collecting, storing, and analyzing data have become insufficient in managing the rapidly growing volume of data. To handle the big data, there is need to design efficient frameworks to process large amount of data arriving at very high speed from various sources. The big data offers both an opportunity as well as a challenge to researchers to deal with huge data [3].The motive for big data implementation is to store data, retrieve the valuable information and constructing features from raw data.
Cloud Computing
Cloud computing is a type of computing and it is used for the delivery of hosted services over the Internet. In other words, Cloud computing relies on sharing computing resources and hardware’s rather than having personal devices or local servers to manage the real time applications .
Components of cloud computing are offered by Cloud Providers that include Infrastructure as a Service (IaaS), Software as a Service (SaaS) or Platform as a Service (PaaS).
Cloud computing is defined by five attributes as Multitenancy ,Massive Scalability, Elasticity, Pay as You go and Self-Provisioning of resources. Nowadays, due to the present availability of low-cost computers, high-capacity networks and storage devices as well as the widespread adoption of hardware virtualization, service-oriented architecture, and autonomic and utility computing have led to a growth in cloud computing. [4].
Big Data and Cloud
Big Data requires large volume of storage space. But the cost of storage space continues to turn down; the businesses posing financial difficulties for the resources are required to influence big data. Storage using cloud computing is a feasible alternative for small to medium sized businesses considering the use of Big Data analytic techniques. [5]
II. SECURITY ISSUES ASSOCIATED WITH BIG
DATA IN CLOUD COMPUTING
1) To protect and prevent huge size of confidentialbusiness, government, or regulatory data from malicious intruders and advanced threats.
2) Lack of awareness and standards about how cloudservice providers securely maintaining the huge disk space and erase existing big data.
3) Lack of standards about auditing and reporting ofbig data in public cloud.
4) The fact that sensitive cloud resources can beaccessed from anywhere on the Internet therefore strong authentication and authorization becomes a critical concern. Different Encryption/Decryption techniques and authentication methods such as administrative rights
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for nodes are implemented to provide authenticity to the user as well as data.
III. RESEARCH CHALLENGES
Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness.
• Networks have increased and require even greaterlevels of security than ever before. Traditional security measures include firewalls and intrusion detection systems were designed to protect facilities and equipment with defined parameters that have with minimal entry points into the networks and devices.
• Cloud-based environments have multiple datacenters, spread across multiple vendors that are managing different categories of datasets which need to be available to different users with different access rights. As a result, firewalls and IDS tools are configured to permit traffic to remote users’ tablets, mobile devices and laptops from anywhere they are. This would reduce effectiveness of the legacy security tools.
• In order to achieve integrity, confidentiality andavailability of these diverse systems and datasets, organizations need to change their security mechanisms from these legacy perimeter and detection-based tools to a focus on implementing efficient protection at the data levels and application levels.
IV. REVIEW
Objective of literature review is to identify the existing security mechanism to handle the security issues and to find the efficient solution to maintain the integrity of data and privacy of user’s personal information from intruder. [9-20].
Table1: Literature Review
Year Author/s Title
2017 Mehdi Sookhak, Abdullah
Gani, Muhammad
Khuram Khan,Rajkumar
Burya
Dynamic remote data
auditing for securing big
data storage in cloud
computing
2017 Deepak Puthal, Surya
Nepal, Rajiv Ranjhan,
Jinjun Chen
A dynamic prime number
based efficient security
mechanism for big sensing
data stream.
2016 Yoon-Su Jeong,Seung-Soo
Shin
An efficient authentication
scheme to protect user
privacy in seamless big
data services.
2016 Yong Yu,Liang lue,Man
Ho Au,Willy usilo
Cloud data integrity
checking with an identity
based auditing mechanism
from RSA
2016 Zheng Yan,Wenxiu
Ding,Xixun Yu,Haiqi Zhu
,and Robert H.Deng
Deduplication on
encrypted big data in cloud
2016 Yibin Li,Keke Gai,
Longfei Qiu, Hui Zhao
Intelligent cryptography
approach for secure
distributed big data storage
in cloud computing
2016 Zhiwei Wang,Cheng
Cao,Nianhua Yang, Victor
Chang
ABE with improved
auxiliary input for big data
security
2016 Muhammad Uaman , Mian
Ahmad Jan,Xiangjjan He
Cryptography-Based
secure data storage and
sharing using HEVC and
public clouds
2016 Yinghui Zhang ,Xiaofeng
Chen,Jin Li,Duncan S.
Wong,Hui Li,IIsun You
Ensuring attribute privacy
protection and fast
decryption for outsourced
data security in mobile
cloud computing
2016 Wei Song, Bing Wang
,Qian Wang, Zhiyong
Peng,Wenjing Lou, Yihui
Cui
A privacy preserved full
text retrieval algorithm
over encrypted data for
cloud storage applications
2016 Uthayanath Suthakar ,Luca
Magnoni, David Ryan
Smith, Akram Khan, Julia
Andreeva
An efficient strategy for
the collection and storage
of large volumes of data
for computation
2015 Gang Chen,Sai Wu,Yuan
Wang
The evolvement of Big
data systems:from the
perspective of an
information security
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application
2015 Jun Sho ,Rongxing
Lu,Xiaodong Lin,Kaital
Liang
Secure bidirectional proxy
re-encryption for
cryptographic cloud
storage
2014 Chingfang Hsu,Bing Zeng
,Maoyuan Zhang
A novel group key transfer
for big data security
V. TOOLS and TECHNOLOGY
Large amounts of data generated from various sources are not organized and straightforward. This section examines various important processing technologies and methods to handle big data in practice [23].Description of different tools that are used to handle large amount of data is described in table 2.
Table 2 : Batch Processing Tools
Batch Based
Processing Tools
Description
Hadoop It performs the data-intensive application
processing. It uses a Map/Reduce
programming Model.
Skytree Server It processes large amount of data at a very
high speed. The main focus of Skytree
Server is real-time data analytics.
Talend open Studio It provides a graphical environment to
conduct an analysis for big data
applications.
Jaspersoft It provides fast data visualization on
renowned storage platforms such as
including Mongo DB, Couch DB,
Cassandra, Riak, Redis, and Hadoop.
Dryad To improve the capability of processing
from a small to a large number of nodes for
parallel and distributed programs.
Table 3: Stream Processing Tools
Stream Based
Processing Tools
Description
Storm To perform real-time processing of large
amounts of data
Splunk Generates reports that capture indexes
and correlates with real time data.
S4 Data stream processed efficiently.
SAP Hana Analysis of business processes in real
time
SQL stream s-
server
To analyze the data of services and log
files data in real-time processing
VI. ANALYTICAL TECHNIQUES
Analytics will play an important role in making sense of big data in real world. This will, in turn, aid the mining of heterogeneous data sets for revealing hidden knowledge, patterns, and relationships. Big data requires development in algorithm and architectures to cope with the associated challenges of high dimensionality, velocity, and variety [24].Big data techniques are required to efficiently analyze large amounts of data within a limited time period. Currently, few techniques are applicable to be applied on analysis purposes given in Table 4.
Table 4: Analytical Techniques
Analytical Techniques Description
Machine learning To evolve behaviors based on empirical
data.
Data mining Extracting useful information or
knowledge from the structured/
unstructured data and databases.
Social network To view social relationships in terms of
network theory
Web mining To discover a pattern from large web
repositories
Optimization methods To solve quantifiable problems.
Associative rule learning To discover relations between variables
in large databases.
VII. CONCLUSION
Big Data requires large volume of storage space. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. From the existing research studied, we observed that big data storage in cloud computing environment needs to be improved. This research work is mainly focused on security issued associated with big data and find out the existing solutions to improve the security mechanism. Deduplication of data will be done to provide data availability to reduce the storage space.
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REFERENCES
[1] P.Jain, M.Gyanchandani and N.Khare, Big data privacy:a technological perspective and review. Journal of BigData .2016, pp. 3-25.
[2] I.Yadooq, I.A.Hashem, A.Gani, Big data: Frombeginning to future: ELSEVIER,2016, InternationalJournal of Information Management,pp.1231–1247.
[3] A.Ali, J.Qadir, R.U.Rasool, A.sathaiseelan, A.Zwitter,Big data for development: applications and techniques:Big Data Analatyics,2016,pp.1-24.
[4] S.Sahar, D Ibrahim, A Review on Cloud Computing andInternet of Things: International Journal of Computer,Electrical, Automation, Control and InformationEngineering Vol:11, No:4, 2017
[5] G. Skourletopoulos , C. Mavromoustakis,, G. Mastorakis,
et.al, Big Data and Cloud Computing: A Survey of
the State-of-the-Art and Research Challenges
:SPRINGER 2017, Advances in Mobile Cloud
Computing and Big Data in the 5G Era. pp 23-41.
[6] G.Somani ,M.S.Gaur,D.Sanghi et.al, DDoS attacks in
cloud computing : Issues,taxonomy, and future
directions:ELSEVIER,2017,ComputeCommunications, pp 30- 48.
[7] I.Lee, Big data: dimensions , evolution impacts and
challenges: ELSEVIER 2017 , Business Horizons.
[8] D.Broeders,E.Schrijvers,B.Sloot,et.al, Big data and
security policies :towards a framework for regulating
the phases of analytics and use of big data:
ELSEVIER 2017 ,Computer Law and security review.
[9] U.Suthakar, L.Magnoni, D.Ryan ,A.Khan,et.al, An
efficient strategy for the collection of large volume of
data for computation: Springer 2016,Journal of Big
Data pp 3-21.
[10] Y.Li,K.Gai,L.Qiu,H.Zhao,Intelligent cryptography
approach for secure distributed big data storage in
cloud computing: ELSEVIER 2016,Information
Sciences.pp 1-13.
[11] M.Sookhak,M.khuram,A.Gani,R.Burya, Dyanamic
remote data auditing for securing big data storage in
cloud computing :ELSEVIER 2017,Information
Sciences.
[12] Y.Yu,L.Xue,M.Au,W.Susilo,et.al, Cloud data integrity
checking with an identity based auditing mechanism
from RSA: Future Generation Computer Systems 2016.
[13] M.Usman, M.Ahmad ,X.He ,Cryptography-based secure
data storage and sharing using HEVC and public
clouds : ELSEVIER 2016,Information Sciences.
[14] W.Song ,B.Wang, Q.Wang,et.al,A privacy preserved
full-text retrieval algorithm over encrypted data for
cloud storage applications: Journal of Parallel and
Distributed Computing 2016.
[15] Z.Wang, C.Cao, N. Yang , et.al, ABE with improved
auxiliary input for big data security
ELSEVIER 2016, Journal of Computer and System
Sciences.
[16] Y.Zhang, X.Chen, J.Li,et.al, Ensuring attribute
privacy protection and fast decryption for outsourced
data security data security in mobile cloud computing:
ELSEVIER 2016 , Information Sciences. pp 17-37.
[17] Z.Yan, W.Ding.X.Yu,H.Zhu ,et,al, Deduplication on
encrypted data in cloud: IEEE Transactions on Big
Data.2016.
[18] C.Hsu,B.Zeng,M.Zhang, et,al, A Novel group key
transfer for big data security :ELSEVIER
2014,Applied Mathematics and Computation pp
436-443.
[19] D.Puthal,S.Nepal,R.Ranjhan,et.al,A dynamic prime
number based efficient security mechanism for big
sensing data stream: ELSEVIER 2017, Journal of
Computer and System Sciences.
[20] G.Chen, S.Wu,Y.Wang, The evolvemet of big datasystem from the perspective of an information
security application: ELSEVIER 2015,Big DataResearch.
[21] J.Sho,R.Lu,X.Lin,et.al,Secure bidirectional proxy re-
encryption for cryptographic cloud storage:
Pervasive and Mobile Computing 2015.
[22] Y.Jeong, S.Shin, An efficient authentication scheme to
protect user privacy in seamless big data services:
SPRINGER 2015,Wireless Press Commun.
[23] C.Tsai, C.Lai, H.Chao, et.al big data analytics: a
survey: SPRINGER 2015, Journal
of Big Data, pp 2-21.
[24] A.Gandomi, M.Haider, Beyond the hype :big data
concepts ,methods, and analytics: ELSEVIER 2015,
International Journal of Information Management pp
137-144.
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Constraints and Limitations in Software
Reliability Prediction1Dr. Archana Kumar2Abhinav Juneja,3Sapna Bajaj
1Director,DITM, Gannaur
2,3Ph.D. Scholar,AFSET,Faridabad
Abstract-Software as entered into our lives in all
aspects. We are now so dependent on technology that it
has become very critical for our lives. All the modern
technological development, be it in any field
incorporates the usage of software directly or indirectly.
Due to this the need for reliable softwares is also gaining
importance. A software in deeply tested before being
brought into its professional application but still it is
very difficult to guarantee its reliability and own that its
is 100 percent free from bugs and will not lead to
failures. In this paper we have discussed various aspects
of software development which are very critical for its
reliability and the various limitations associated with
the true prediction of the degree to reliability of the
software. There is always a possibility that the Software
may have latent bugs even after passing all levels of test.
There has been a lot of research in this area and
statisticians have given a number of Software
Reliability Growth Models for reliability prediction,
still there are issues while choosing the appropriate
model and making a compliance with the standard
assumptions of the selected models. Due to this
reliability prediction accuracy is still a complex area.
Index Terms Calender Time, Error, Failure
data, Failure Rate, Hardware Reliability,
Software Reliability.
1. INTRODUCTIONComputers are bringing revolutionary changes to
our life with their involvement in most human-made
systems through sensing, communication, control,
guidance and decision-making. When the requirements for and dependencies on computers increase, the crises of computer failures also
increases. The impact of hardware and software
failures range from issues like malfunctions of home appliances, economic loss like compromise of banking systems to life-critical like failures of flight
systems and medical software. As the applicability of computer operations becomes more essential and complicated in the modem society, the need for reliability of computer software becomes more
important and critical. In fact, computer software had
already become the major source of reported outages in many systems. This trend has been stressed by the
fact that hardware components of a system become
increasingly reliable, and software starts to dominate
the cause of computer system failures and outages.
With the increase in demand of Software driven
application gadgets, its size, complexity, and criticality also increases. Today, the growth in
utilization and dependency on software components
is largely responsible for the high overall complexity of many system designs, since it is the integrating potential of software that has allowed designers to contemplate more ambitious systems encompassing a broader and more multidisciplinary scope[1,3,4].
The intention of this paper is to describe the Software Reliability Engineering, its various implementation techniques and the limitations in prediction of reliability associated with any so called tested software. There is a lot of gap between the reliability that we can predict for our software under testing and the actual reliability encountered when the software is actually tested and operated in the actual user environment. The paper unfolds certain aspects that limit the analysts in accurate prediction of inherent faults in the software. We start our discussion with a brief distinction between software and hardware reliability. After that we appreciate the underlying differences between fault and failure associated with a software. In the proceeding sections we discuss the reliability incorporated at different stages of software development process , overview of different software reliability models based on their approach to make software reliable and then we finally discuss the underlying unreliability which still limits and constraints the accurate prediction of remaining and latent errors in the Software that may or may not lead to its failure at a later stage.
Software versus Hardware Reliability
There are some foundational differences between hardware and software failures that impact the analysis between Hardware versus Software reliability. The primary difference between the two is in the underlying mechanisms causing failures. With hardware, failures are often initiated by physical processes related to stresses imposed by the operating environment. Specifically, failures are due to ageing or the components degrading with time lapse, deteriorating, or being subjected to environmental
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shocks. Contrastingly in Software, there is nothing to “wear out”. The major stimulus mechanism for failures are the remaining faults within the software which have not either been expected or unfolded. These faults may be the result of errors in coding or implementation of design or requirements specifications. However, just the presence of a fault is not enough to cause a failure. First the software must be executing, and second the input being processed during execution must be such that the fault will be encountered under just the right set of conditions that result in a failure[1]. Reliability isgenerally a key driver in many system performance criteria, e.g., mission reliability and life-cycle cost[2]. Similarly, reliability and quality analysis within each stage of the design process can greatly reduce the life-cycle cost of a product.
Failure in contrast with Fault
Maintaining a clear demarcation between failures and faults is very critical while applying Software Reliability. Since Software Reliability is integrally associated with Software Failures, any discussion of software reliability must start with a definition of Software Failure [1]. A formal definition of a Software Failure is a deviation of the operation of a software system from its requirement specification. While using the concept of Software Reliability, The notion of requirement specification is not limited to paper agreements done while preparing the Software Requirement Specification rather may be extended to cover anything relating to the customer’s satisfaction with the product to be more precise it should be a validation by the customer and not mere verification. Examples of software failures might be simply the absence of part of the output report or some format even, Some software failure might result in the system being completely inoperable but recoverable by reinitializing the system software and any such condition not desired by the customer may be associated with software failures.
Conversely, a fault is a defect that has not been discovered in the software during its different phases of development. Examples of a fault might simply be an uninitialized variable ,an incorrectly coded program statement, an incorrect implementation of a design or requirement specification. Software failures are an external manifestation of the presence of a fault. However, there is not necessarily a one-to-one correspondence between fault and failure. A fault may stimulate many failures if the fault is not neutralized after the first failure is encountered. Also, different faults may cause failures to occur at different rates. On the other hand, a fault may stimulate no failure at all This would be the situation if the customer uses the software product in a way that the fault is never encountered under the right conditions to cause a failure. Customers are not
concerned per se with how many faults there are in a software product. They are concerned with how often the software will fail for their intended use and how costly each failure will be to them.
II.INCORPORATING RELIABILITY IN
SOFTWARE
The software product life cycle into four phases: product definition, product design and implementation, product validation, and product operation and maintenance [1].
I. Definition of Product
Proper product definition is essential for having a successful product on the market. The primary output of the definition phase is a requirement specification for the product. Reliability objectives should be included as an explicit part of the requirements specification. The first step in setting reliability objectives is to define what a failure is from the customer’s perspective. Next, failures should be categorized by the impact they have on customers. A key step is understanding customers tolerance to failures of different categories and customers willingness to pay for reduced failure rates in each failure category. The information developed in each of these steps can then be used to develop reliability objectives for the product. After such objectives are established for the product as a whole, they must be allocated among the hardware and software components within the product. In effect, a reliability budget is being established for each of the components within the product. Once reliability objectives are established for software components, the following two important items are needed to proceed.
1.An Operational Profile that determines how theCustomer will use the Product (field of use for the product being developed).
2.Estimates Relating Calendar Time or Execution
Time(CPU time).
2.. Design And Implementation Of Product
The primary purpose of the design and implementation phases is to turn a requirement specification for a product into a design specification, and then to implement the design into the product. One activity during the design phase is allocating and budgeting software reliability objectives among software components. An analysis must be conducted to ascertain whether the reliability budget can be attained within the proposed design. Another important activity should be to certify the reliability of “reused’’ software (not only application software but also system software such as operating system software and communication interface software). There is a strong push to reuse software developed
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for one product within another product. However, the reliability of the reused software may not be sufficient to satisfy the needs of the new product. Before reusing software in a new product, its reliability should be established through reliability testing under the operating conditions intended for the new product. It is important to use the operational profile for the new product in reliability testing. These are many verification activities that should also be going on during the implementation phase. Verification activities such as inspections, unit testing, and integration testing are intended to “verify” that what was actually produced within a development stage is the same as what was intended to be produced. Verification activities, such as inspections, can be applied to such development stages as specifying requirements, design, and coding (unit implementation).The intention of verification activities is to minimize the propagation of the number of faults introduced in one development stage to another stage. At this time, there is little that can be done to estimate software reliability using measures from such product verification.
III. Product Validation Phase
The primary thrust of validation activities is to certify the product is suitable for customer use. Product validation is associated with evaluating a software product at the end of development to ensure it complies with the initial requirements for the product. Product validation activities for software products generally include system test and field trial activities. Software reliability measurements are particularly useful in this phase in conjunction with reliability testing to monitor the progress of testing and to help in making product release decisions. The sequence of activities during testing typically proceeds as follows. Failures and the corresponding execution time (from the start of testing) are recorded
during testing, Statistical techniques are used to estimate the parameters of software reliability execution time model components based on the
recorded failure data.
IV. Product Operations and Maintenance
The primary thrust of the operations and maintenance phases is to transfer the product into the
customers day-today operations, to support the customer in the use of the product, and to repair or
fix faults within the software that are impacting the customers use of the product. The reliability of the
currently operating software should be great enough so that introducing the new software release will not
reduce the reliability below a “tolerable” level for the end user of the software product. Failure data
collected in the customer’s operating environment can be used to verify the customer’s perceived level
of reliability to the measured reliability of the
product.
IV.SOFTWARE RELIABILITY ASSESSMENT
MODELS
Nearly all existing models of software reliability
may be categorized into four basic types[6]:
a)Failure Count Category: These models take into
account number of faults or failures uncovered in
specific intervals of time. Keys assumptions of these
models here are that the testing intervals are independent to each other, faults uncovered during
non overlapping time interval are independent of
each other[6].Typical examples of these models are
Shooman’s exponential model, GO-NHPP model.
b)Time between failure Category: These modelsprovide estimate of the times between failures. Major assumptions of these models include independent times between failures, equal probability of exposure for each fault, no new faults injected during correction. Typical examples of such models are Goel and Okumoto’s imperfect debugging model.
c)Fault Seeding Category: This type includesmodels to predict number of faults in the program at initial time via seeding of external faults. Mojor assumptions in these models include that seeded faults are distributed randomly in the program and seeded faults have equal probability of being detected. Typical example of this category is Mill’s seeding model.
d)Input Domain based category: These modelspredict the reliability of a program when the test cases are sampled randomly from well known operational distribution of input program. Major assumptions here are that input profile distribution is known, random testing is used, we may partition the
input domain into equivalence classes. Typical example of this category is Nelson’s model.
Constraints and Limitations in Accurate Software
Reliability Prediction
Although Software reliability is accepted as a key attribute in software quality, and is defined as the probability of failure free software operation for a
specified period of time in a specified environment
.The residual faults in the software system directly contribute to the failure rate, causing software unreliability. The problem of measuring software
unreliability can be approached by obtaining the
estimates of the residual number of faults in the software. Assessing software reliability in a testing phase of a software development process is one of the
important issues to develop a highly reliable software system[7]. The number of faults that remain in the
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68
code is also an important measure for the software
developer, from the point of view of planning
maintenance activities
[3].Most of the reliability models which have
been designed so far have been designed keeping in
view the assumptions that
1.A software fault is fixed immediately upon
detection, and no new faults are introduced during the debugging process. This assumption of instantaneous
and perfect debugging is impractical, and should be
amended in order to present more realistic testing
scenarios.
2.The time lag between the detection and
debugging of a fault is not explicitly accounted for in the traditional software reliability models, as it complicates the failure process significantly, making it impossible to obtain closed-form expressions for various metrics of interest. However, the estimates of the residual number of faults in the software is influenced not only by the detection process, but also by the time required to debug the detected faults.
3.Debugging process affects the number of faultsremaining in the software and consequently its reliability, and makes a direct impact on the quality of a software product.
4.The other stringent assumption is that of perfectdebugging. Studies have shown that most of the faults encountered by customers are the ones that are reintroduced during debugging of the faults detected during testing. Thus imperfect debugging also affects the residual number of faults in the software, and can at times be a major cause of its unreliability, and hence customer dissatisfaction.
5.Conventional Software reliability models cannot account for this difference between the detected and debugged faults[2], simulation offers a
powerful, yet simple alternative to take this
difference into consideration.
There is a need to design such models which keep into account the reliability deterioration due to
debugging process.
V.CONCLUSION
Software Reliability prediction is a very complex process and it needs the selection of an applicable
Software Reliability Growth Model(SRGM) to give a
reliable prediction. An SRGM may suit one particular Software Development but may fail in the other.The standard assumptions taken into account by SRGM’s
are also not validated in actual run time environment. There is no thumb rule to quantify a particular model
that may suit well for all softwares under
development.
REFERENCES
[1] William W . Everlett “Software Reliability Measurement”. IEEE transaction on selected areas in communication Vol.8,No.2, Feb 1990.
[2] Stephen W. Ormon, C. Richard Cassady, and Allen G.Greenwood” Reliability Prediction Models to Support Conceptual Design”, IEEE Transcactions on Reliability,VOL. 51, NO. 2, JUNE 2002
[3] Swapna.S.Gokhale , Michael R. Lyu , Kishore S. Trivedi“Software reliability analysis incorporating fault detection and debugging activities” IEEE.
[4] Wen-Li-Wang, Mei–Hwa Chen”Heterogeneous Softwarereliability Modelling”,proceedings of the 13th international symposium on software reliability engineering (ISSRE’02).
[5] John D.Musa “Introduction to Software Reliability Engineering and Testing” article in IEEE,1997.
[6] Zuzunz Krajcuskova,”Software Reliability models”, Deptt. Of Radio Electronics, Slovak University of Technology,SlovakRepublic,IEEE 2007.
[7] Shinji Inoue, Member, IEEE, and Shigeru Yamada, Member, IEEE,”Generalized Discrete Software Reliability Modeling With Effect of Program Size” IEEE Transactions on Sysbernetics-Part-A:Systems and Humans,Vol. 37, NO. 2, MARCH 2007.
[8] Google.com
[9] Amazon.com
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
69
A REVIEW ON THE INTELLIGENT SCHEMES FOR
AUTOMATIC GENERATION CONTROL IN MODERN POWER
SYSTEM
SALONI
Assistant Professor
Dept. of EEE, BMIET, Sonepat
VISHAL JAIN
Associate Professor
Dept. of EE, BMIET, Sonepat
Dr. DEVENDER SAINI
Assistant Professor
Dept. of EE, UPES, Dehradun
Abstract— Automatic generation control (AGC) is
one of the important control problems in the design and
operation of interconnected power system, and is
gaining popularity today due to the growing size,
varying structure, upcoming renewable energy sources
and new uncertainties, and complexity of power
systems. AGC system requires increased intelligence
and flexibility to guarantee balance between load and
generation. The modern AGC systems surely must be
intelligent, and should be capable of handling complex,
multi-objective optimization problems characterized by
diversification in policies, control strategies. The
foundation of such systems should be based on
intelligent algorithms, advanced devises, and rapidly
changing Information technology. This paper presents a
review of the intelligent algorithms used in the modern
AGC by the researchers.
Keywords--Automatic generation control (AGC),
Area control error (ACE)
I. INTRODUCTION
Operations in actual power system is dynamic where, the load is continuously changing. Due to the physical and technical constraints the generation is not able to deal with the changing load; as a result there is a disparity between the actual and the scheduled generation which is known as frequency error [1]. Automatic generation control (AGC) is a major control system that maintains stability between the load and generation in power systems at nominal cost [2]. The main task of AGC system is to maintain nominal frequency, power interchange, and economic dispatch.
Intelligent automatic generation control offer a systematic understanding of the fundamentals of power system AGC, and proposes various new schemes using intelligent control methodologies for minimizing the system frequency deviation and tie-line power changes, which is essential for the operation of interconnected power systems [3-4]. The coming sections illustrate various intelligent control strategies for modern AGC.
II. TECHNIQUES USED FOR AGC
A. Fuzzy Logic AGC
Today Fuzzy logic is extensively used in almost all fields of operation and control because of robustness and reliability. The conventional control strategies are generally based on the linearized mathematical models of the systems to be controlled; however the fuzzy control methodology is based on the expert knowledge and experiences of the domain. Vast literature is available for the fuzzy-logic-based AGC design [5].
A lot of fuzzy logic controller structures exist for AGC, depending upon the number and type of inputs outputs, fuzzy sets, membership functions, control rules and type of deffuzification method.
The Fuzzy logic controlled AGC applications are classified into three classes:
(1) Dynamic fuzzy controller known as fuzzylogic controller (FLC),
(2) Fuzzy logic system used for tuning the gainsa proportional integral (PI) (or proportional-integral-derivative [PID]) controller
3) Fuzzy logic used for economic dispatch.
Fig. 1 FLC Structure for AGC
B. Neuro-Fuzzy and Neural-Networks-
Based AGCLearning capabilities of neural networks are used
to tune the fuzzy controllers enabling them to be adaptive. The synthesis of neural networks and fuzzy logic builds a neuro-fuzzy controller, which uses a learning algorithm based on the training samples available in the neural network theory [6]. The
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
70
combination of ANN and fuzzy logic is presented in several research articles applied for intelligent-based AGC design and the novelty in both designs are utilized in a single hybrid AGC system.
Fig 2: A Hybrid Neuro Fuzzy Controller for AGC
C. Genetic-Algorithm-Based AGCA genetic algorithm (GA) is a bio-inspired
algorithm that is based on natural selection, genetics and evaluates the fitness of individuals. The GAs are a general purpose optimization algorithms which has been extensively employed over the years to solve complex powers. Fig 3 depicts the basic evolution process starting from the random initialization of population, selection, crossover, and mutation. The loop continues till the average fitness function of the whole population improves.
Fig. 3. A Simple GA Flowchart
GAs have been efficiently used to tune the parameters for diverse AGC schemes, e.g., gains of PI, PID, FLC etc.[7]. GA prove to be a suitable optimization technique to tune the membership functions, and rule sets for fuzzy gain scheduling of
frequency controllers of interconnected power systems, thus improving the dynamic performance.
Fig. 4 Use of GA for AGC controllers
D. Hybrid Intelligent Techniques in AGC
In view of latest advances in Artificial Intelligence in control and evolutionary computations, various hybrid intelligent control methodologies have been proposed to address the problem of AGC [8].
Various hybrid techniques such as GA-simulated annealing (SA)-based fuzzy AGC scheme, a particle swarm optimization (PSO) combined with fuzzy technique, the reinforcement learning (RL) approach is used to design AGC system [9-11]. The literature reveals that the performance of hybrid techniques is better as compared with the simple GA techniques and classical methods.
III. CHALLENGES IN MODERN AGCDeregulation and introduction of new uncertainty
and changeability in the power systems add new challenges and economical reforms linked with AGC systems synthesis and analysis. With the growth of the electric industry, huge generation if power is required and a lot of efforts will be needed to effectively handle these distinctive operating and planning characteristics. A key facet will be to make the AGC system robust and utilize the energy resources.
The modern AGC system must be able to handle complex exchanges between control areas, grid, interconnections and changes in the generating capacity and load demand. It is imperative to have an intelligent controller based on advanced computing algorithm to realize optimal and adaptive AGC schemes.
IV. CONCLUSIONThe problem of AGC in power systems can be
simply handled using artificial intelligent techniques by converting them into performance optimization problem. This has led to emerging trends of application in computational intelligence and soft
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computing in the power system AGC. The most important intelligent AGC methodologies based on fuzzy logic, neural network, neuro-fuzzy, genetic algorithm, and hybrid techniques have been discussed in this paper. The challenges for modern power system are listed giving directions for further research in the field of intelligent AGC.
REFERENCES
[1] O. I. Elgerd, “Electric Energy Systems Theory anIntroduction,” 2nded. New Delhi, India: TataMcGraw-Hill, 1983, pp. 299–33. [2] Hadi Sadat,“Power system analysis”, Tata McGraw-Hill, Edition2002.
[3]N.Jaleeli, et.al."Understanding Automatic GenerationControl" IEEE Transactions on Power systems, vol. 7,pp. 1106-1122, August 1992.
[4] D.M. Vinod Kumar, “Intelligent controllers forAutomatic Generation Control”, IEEE, pp. 557-574,1998.
[5] G.A. Chown and R.C.Hartman, “Design andExperience with a Fuzzy Logic Controller forAutomatic Generation Control (AGC)”, IEEETransactions on Power Systems, Vol.13, No.3, pp.965-970, August 1998.
[6] S. Bhongade, H. O. Gupta, and B. Tyagi, “Artificialneural network based automatic generation controlscheme for deregulated electricity market,” in 2010Conference Proceedings IPEC, 2010, pp. 1158–1163.
[7] Y. L. Karnavas, “AGC Tuning of an InterconnectedSystem after Deregulation Using GeneticAlgorithms”,vol. 2005, pp. 218–223, 2005.
[8] H. Bevrani et.al, “Intelligent Automatic GenerationControl: Multi-agent Bayesian Networks Approach”,2010 IEEE International Symposium on IntelligentControl Part of 2010 IEEE Multi-Conference onSystems and Control Yokohama, Japan, September 8-10, 2010.
[9] Hou Guolian, Qin Lina, Zheng Xinyan, and ZhangJianhua, “Application of PSO-based fuzzy PIcontroller in multi-area AGC system afterderegulation,” in 2012 7th IEEE Conference onIndustrial Electronics and Applications (ICIEA),2012, pp. 1417–1422.
[10] K. Wadhwa, J. Raja, and S. K. Gupta, “BF basedintegral controller for AGC of multiarea thermalsystem under deregulated environment,” in 2012 IEEEFifth Power India Conference, 2012, pp. 1–6.
[11] S. Pati, B. K. Sahu, and S. Panda,“Hybrid differentialevolution particle swarm optimisation optimised fuzzyproportional–integral derivative controller forautomatic generation control of interconnected powersystem,” IET Gener. Transm. Distrib., vol. 8, no. 11,pp.1789–1800,Nov.2011.
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Abstract— Internet of Things is a today’s Technology.
Research on this technology is in early ages. In IOT Ocean, it is a
collection of devices, domains, components, middleware, and
protocols. IOT is such atechnology, which is improving day by
day. IOT is now becoming a part of daily human life. It opens
new challenges and research area for researchers. This paper is
just aneffort to find research possibilities and identifying breaths
and diversity of existing IOT research in various fields. This
paper reviews the architecture of IOT domains, elements,
standards and platforms. Themain purpose of this paper is to
summarize the analysis of recent trends in IOT domain. Here.
This effort is somewhat different other survey papers because
ofitspresent research possibilitieswith research variables of each
domain of IOT.It will be helpful for theresearcher to find new
research areas in IOT.
Protocol Keywords— RFID- Radio Frequency Identification,
IOT- Internet of Things,CoAP- Constrained Application
I. INTRODUCTION
Kevin Ashton first proposed the term Internet of Things in
1999. Internet of Things is such a technology in which data
collected from real life objects and processed intelligently to
make it well informed. The trend of computing is also
moving from static page pages to social networking web and
then ubiquitous computing. IOT enables many of objects on a
global network. IoTpromises to create a global network,
which will support ubiquitous computing. Radio Frequency
Identification (RIFD) and Wireless Sensor technologies
(WSN) will be contributing to fulfilling these challenges. IOT
will empower to connect things with physical entities and
virtual components. Internet of things can be recognized in
three patterns, Middleware, Sensors, and Knowledge. In this
concept, the low layer includes sensors, actuators, cameras
that are collecting the data and pass to the communication
channel. The Middleware is a software layer that receives
the data and processes and then it sends to the IOT repository
by using the internet as a communication medium. Raw data
collected from the communication medium is processed.
Process raw data takes shape of knowledge. [1]
Various researchers have defined in their own ways.
“A dynamic global network infrastructure with self-
configuring capabilities based on standard and interoperable
communication protocols where physical and virtual ‘Things’
have identities, physical attributes, and virtual personalities
and use intelligent interfaces and are seamlessly integrated into
the information network” (Kenenburg,2008) [1]
“A concept: Anytime, anywhere and any media, resulting into
asustained ratio and man around 1:1” (Srivastava 2006). [1]
“Things having identities and virtual personalities operating in
smart spaces using intelligent interfaces to connect and
communicate within social, environmental and user context”
(Networked Enterprises & RFID & Micro & Nano system)”
[1]
In this paper, the survey is focused on finding IOT research
areas and identifying breaths and diversity of existing IOT
research in all fields. This paper is the result of analysis on 200
research papers on IOT. It was publishedbetween 2010-17 and
found the recent trends in IOT and its future perspective.
II. SURVEY OF ELEMENTS OF IOT
This module, we present about Components of IOT. It gives
abrief introduction to various hardware, software, middleware
and service-oriented architecture.
1. Hardware
A. Radio Frequency Identification (RFID)
In IOT, identification of real objectsis of primary
importance. Before IOT, the industry has been used some of
these technologies named Magnetic Ink Character Recognition
(MICR), bar codes, smart cards and magnetic tapes. The
major issues with these technologies are to read from the
reader and put back in original place. Radio Frequency
Identification (RIFD) provide the solutions to overcome the
problem occurring the previous technologies. The RIFD
system consists a radio frequency tag, which holds the
information about things. RIFD tag is attached to the thing to
be identified. Another important part of RFID system is
Reader, which queries the tag using radio frequency waves.
The reader is capable of storing information in the tag and is
capable of altering the stateof the tag. The most important
feature of RIFD technology is non-contact sensors, radio
waves or microwave. RFID tag can be read by a reader from
few cm to few meter far with touching RFID tag which is
attached to a real object. RFID provides small volume, low
cost, low power consumption and high reliable devices. There
are two types of RFID tags: active and passive tags. Active
RFID tags have a small battery with RFID chip. On the other
hand, passive RIFD tags active when they come contact with
radio wave e.g smart cards. RFID technology can be used in
IOT. Real objects with RIFD technology connects the Internet
as a thing in IOT Architecture. The major issue regarding is
Rajeev Kapoor, Jagpreet Singh Sidhu, Dr. Subash Chander
Assistant Professor, Punjabi University Neighbourhood Campus Jaito
Associate Professor, CUIET, Chitkara University, Punjab, India,
Assistant Professor University College Jaito, Faridkot
Internet of Things: A Survey of Architectures
and Recent Research Trends
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73
that addressing scheme used for identifying the real object.
There are two approaches commonly used one is Electronic
Product Code (EPC) and another is Ubiquitous Identifier
(Uid).[2]
A.1 Electronic Product Code
Electronic Product Code (EPC) is known as the universal
identifier of real objects. This technique provides a unique
identity to every real object anywhere in the globe. Industries
follow the EPCglobal Tag Data Standard which is a common
structure for identifying all objects. The Structure of
EPCglobal Tag Data Standard is given in the Figure1.
Figure 1 EPC Message Format
This structure contains four parts: Header, EPC manager
number, Object Class, Serial Number.Header: It contains
length, type, structure, version, and generation of EPC
code.EPC Manager Number: Entity is responsible for
maintaining the subsequent partitions. Object class- It
identifies a class of objects. Serial Numbers –Identifies the
instance of the product
EPC identifiers presently support seven identification keys
which are known as GS1 System of Identifiers. This EPC
Code can be used in the RFID tags. The low-cost passive
RFID Tags are specially designed for this purpose.EUPC Code
is written in RFID Tag chip which has Binary Encoding.In the
survey of EPC code with RFID Tag, it provides the industry
standard which provides object visibility. It provides object
location and status of the object in the real time.[3]
A.2 Ubiquitous Identifier (Uid)- Ucode
Ucode is used as another alternative identifier of IOT things. It
is 128 bits ubiquitous code which is used for an identifier for
real life objects. These are assigned to tangible objects of the
real world and are stored on the tag. This tag is known as
ucode tag and used in RIFD Tags, Smart cards. The normal
length is 128 and can be extended by an integral multiple of
128,256,384 and 512. The Unicode has five fields which are
given below
Ucode = Version+Top level code(TLDC) + class code+
second level domain code (SLDC) +Indenfication code. [3]
B. Near Field Communication (NFC)
NFC is an improved version of RIFD Technology, known as
Near Field Communication NFC Devices. These are very
short range communication standard where devices
communicate
each
TABLE I
SOA Architecture of IOT[5]
Layer Name Description
Sensing
Layer
This Layer is responsible for
interaction with existing hardware
RFID, NFC, Sensors, actuators. It
also responsible for gathering
information from these devices. It
means sensing the data from the
Real World
Networking
Layer
This layer is responsible for
providing the basic network
support to things. It provides the
best route for communication in a
Heterogeneous network by using
wired or wireless network.
Service
Layer
This Layer plays the main role in
this architecture. It is responsible
create the service and manages
services. It provides the services
to the user as per their needs.
This Layer includes the key
components
a. Service Discovery
It means a service which
finds objects that can
provide the needed
service and data in the
proper way.
b. Service Composition
It provides that service
which is enabled to
communicate and
interact with all
connected things.
c. Trustworthiness
management
It provides such service
which device trust and
reputation mechanism
which evaluates the
information of two
things.
d. Service API
This component
provides the service to
the user, they can
interact with the system
through API interfaces.
Interface
Layer
This Layer provides interaction
methods of API to the user. it
also provides a method how can
application can interact with
system Interfaces by API
programs.
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74
other when touching each other or come near each other.
Each NFC tag contains Unique Identification (UID), which
shows device uniqueness. This technology is using inNFC in
a smartphone for transferring data with each other. Another
type
of NFC tag is a passive one which becomes active when radio
waves are passing through thetag and transfer the data to each
other.
C. Wireless Sensor Network (WSN)
Jennifer Yick [4] explains Wireless Sensor Network is a key
technology in IOT. It is the network of Smart Wireless
Nodes. All smart nodes are connectedwireless gateway. These
nodes contain small sensors with limited processing capacity.It
also contains the limited and in expensesresources than
traditional sensors. They are capable of sense, measure and
collect information from real life environment. After this
process, they transmit data processing unit for further data
processing. [4]
D. SERVICE ORIENTEDARCHITECTURE OF IOT
Li Da aXuexplains IOT architecture consists Four Layers.
which are Sensing layer, Networking Layer, Service Layer and
Interface Layer. The details are given in Table I. [5]
E. MIDDLEWARE
M. A Razzaque explains, a middleware is a software layer
which provides an interface between a user of applications of
IOT and Infrastructure of IOT. It interacts communications
devices, things, and communication network. It provides
common services for applications. [6]
III. SURVEY ON DOMAIN TREE OF IOT
Figure 2 Smart Society
A. Smart Societies
This module, we present the previous research work is the
done by the researchers in the area of Smart Society in IOT
environment and covers the research gaps and requirements to
improve it on differentparameters. A smart society can be
classified according to the application of technology to
different domains. They are given in figure 2
A.1. Smart City
A Smart City issuchacity in which all physical Infrastructure,
transportation, and social infrastructure are creating a whole
environment with ICT. The data are gathered from sensors of
different infrastructure in the city. These data are sent to
therepository on thecentral server and an intelligent decision
can be made on facts are collected from these data. This
whole environment includes smart infrastructure, smart
surveillance, smart electricity and water distribution, smart
services. The sensing is the main key component which is used
to monitor and provide awareness to how to use their resources
and infrastructure in an efficient way. There are some
communication standards namely Dash 7, Zigbee, LTE, 3G,
and NFCwhich are used to implement this concept. [7] The
Author [8] provides a complete vision to theway of using
sensing technologies in smart cities and describe the how to
implemented in water distribution system, Electricity
distribution systems, smart building, and homes. [8]
A.2 Smart Home
The smart home is an intelligent and automated building. It is
equipped with smart objects. All smart objects connect with a
residential gateway. The data from smart objects are transfer to
the server through a residential gateway. This server is a
repository of information and includes algorithms used for
taking intelligent decisions. This technology can monitor the
internal environment and activities of doing in the house. The
author Baoan LI [9] describes other issues regarding family
security, Family medical treatment, family data processing,
family entertainment and family businesses. For the family
security, it includes cameras, smoke detectors, sensors etc.
For Family healthcare, household medical devices are
connected to IOT network and family doctors, hospitals. This
facility is more beneficial for children and elderly peoples
because they can easily monitor health condition by the
doctors and take necessary action when it requires. With
increasing usage of internet, family data is also increasing day
by day. It includes films, audio, video clips etc. this family
data must be stored on repository server which is connected
through IOT technology. The Author [10] Moataz Soliman
describes the system architecture for the smart home. The
author includes some major compo The smart home is an
intelligent and automated building. It is equipped with smart
objects. All smart objects connect with a residential gateway.
The data from smart objects are transfer to the server through a
residential gateway. This server is a repository of information
and includes algorithms used for taking intelligent decisions.
This technology can monitor the internal environment and
activities of doing in the house. The author Moataz
Soliman[11] describes the system architecture for the smart
home. This system architecture includes some major
components like Microcontroller – enable sensors,
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Microcontroller –enable actuators, Data store, Server, API
layer and web applications for this purpose. [10-15]
A.3 Smart Grid
The author Xi Fang describes [17]; the term smart grid is used
improve version of traditional power Grids. Smart Grid is
known as an intelligent grid. It contains features: digital, two-
way communication, distributed generation, Sensors,
throughput, self-monitoring, self-healing, adaptive and
islanding, pervasive control, Many customer choices. Smart
Grids are different from traditional power grids. The
traditional grids have merely job to generate electricity and
distribute to the customers. The Important feature of SG is
two-way commutation, automatically load handling and
enhance security feature. Today SG has three major systems
from technical point view: Smart Infrastructure, Smart
management system and smart protection systems. The
requirement of adopting Smart power grid is improving
reliability and quality, improving resilience to distribution,
automating maintenance and operation, improve grid security
etc. As per thedefinition of National Institute of Standards and
Technology(NIST), “The Smart Grid is a grid system that
integrates many varieties of digital computing and
communication technologies and services into the power
system infrastructure. It goes beyond smart meters for home
and business as the bidirectional flows of energy and two-way
communication and control capabilities can bring in new
functionalities.” [18] The NIST provides a conceptual model
for a Smart Grid. It divides into seven domains. Each domain
contains one or more actors which include devices, systems,
and programs. Customers, Markets, Service providers,
Operations, Bulk Generations, Transmission, and Distribution
are seven domains of NIST conceptual model of Smart
Grid.[19-25]
A.3 Smart Tourism
The term Smart Tourism starts anew era of the tourism in
Tourism Industry. The aim of Smart Tourism provides
meaningful information to tourist at the right time. It also
provides the mobile connectivity for intelligent and
meaningful information between tourist and tourist service
providers. The basic aim of smart tourism improves the
tourism services. It enhances tourism industry and provides
new job opportunities for tourism Industry. In these days,
tourists are using e-services like online booking of hotel,
plane, cab and destination stations. With using the smart
objects, the compile information store at base server point and
deliver to the tourist like road traffic information, weather
information, accommodation information and route
information to tourism. China and developing countries like
India, Nepal, Bhutan, Singapore are taking interest to develop
thetourism industry. China National Tourism Administration
(CNTA) has officially announced “Beautiful China, 2014-
Year of as Smart Tourism”. It is anas important initiative in
China's Tourism to develop smart Tourism for Tourism
Industry. In western countries, they rarely take interest in
smart tourism. [26-30]
A.4 Smart Industry
The use of IOT in the industry, it introduces arevolution in
industrial practice. This begins the era of industry which is
called Industry 4.0. It is known as fourth industrial revolution.
It starts anew era where physical objects contain embedded
electronics like RIFD Tags, sensors etc. These objects are
connected to the Internet. It creates asmart network of smart
objects and plays the active role in business. Management can
watch and monitor the ongoing production to finish product.
This enables machines and plants adapt behavior according to
thesituation and operating condition. [31,32,33,34]
B. Healthcare
Healthcare using IOT things opens a chapter of research in
healthcare. In this concept, healthcare devices are used as
things and connected to the Internet. From this from children
to elderly can avail benefit for smart healthcare services.
According to M.R Isam [35], he has classified IOT in two
Classification One Services and second is Applications of
IOT. Services include ambient assisted living, Internet of m-
Health, Adverse Drug reactions, community healthcare,
Children health information, Wearable device access,
Semantic medical access, Indirect emergency healthcare,
embedded gateway configuration and embedded context
prediction etc. The second classification is Application of
IOT. He further classified in two classification namely Single
condition and Clustered conditions. The single condition
includes Glucose level sensing, ECG monitoring, Blood
Pressure monitoring, Body temperature monitoring and
oxygen saturation monitoring. In the clustered condition
includes rehabilitation system, Medication management,
Wheelchair management, Imminent healthcare and
TABLE II
Design consideration for Industrial IOT
application
Layer Name Description
Energy How long can an IOT device operate
with the limited power supply?
Latency How much time is a need for message
propagation and processing?
Throughput What is the maximum amount of data
that can be transported through the
network?
Scalability How many devices are supported?
Topology Who must communicate with whom?
Security &
Safety
How secure and safe is theapplication?
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Smartphone healthcare solutions etc. The author Luca
Catarinucci [36], propose a Smart Hospital System which
monitoring and tracking patients, personnel and biometric
devices. He proposes SMS architecture in three parts Hybrid
Sensor Network, Smart Gateway System and User Interface
for Local and Remote Users. This system provides power
effective remote patient monitoring and immediate handling of
emergencies. [37-48]
IV IOT STANDARDS
A. Infrastructure protocols
6LOWPAN
6LOWPAN is a Network architecture, whichsupports for tiny
devices in IOTenvironment. It is an open Standard defined in
RFC 6228 which is developed by Internet Engineering Task
Force (IETF). It works on 2.4 GHzband with 250 kbps
transfer speed and creates own Mesh Network. The powerful
feature of this technology is tinydevices which are capable
ofcommunicatingwith theouter world. The structure of this
network is mess Network. In this Network, there are two
devices are used. One name is arouter and thesecond one is
thehost. The job of arouter is determining the best route while
thehost is known as end devices. Hosts act as sleepy devices
which are periodically active and check its parent router for
network existence. It consumes low powercommunication for
implementing this technology. When this technology is
sending data on MAC Layer and Physical Layer, it uses the
adaptation layer for this purpose. This technology uses
header compression, fragmentation & assembly, and stateless
autoconfiguration.[49,50,52]
RPL ( IPV6 routing for low power and lossy Network).
The RPL (IPV6 routing for low power and lossy Network) is
default routing protocol for Internet of Things (IOT). It opens
the doors of Internet for Tiny devices and embedded network
devices. It standardized by IETF in 2011 for establishing
acommon standard for interoperable commercial appliances in
growing IOT age. Each IOT device has anidentification
number. IPV6 has become acommon mechanism to provide
the unique id to each smart device. Therefore, RPL is rapidly
considered as thede-facto routing protocol for IoT devices.
RPL uses the Distance vector routing for storing routing
information. It creates a destination-oriented acyclic
graph(DODAG) which one edge node edge router node,
multiple routers node, and ahost node. It supports two-way
traffic flowspattern from root to devices that are known as
Point-to –Multipointcommunication in RPL. The RPL
protocol works on two operationalmodes: Storing and Non-
storing mode. In the storing mode, every node contains a
routing table which provides amapping between all
destinations node. In the non–storing mode, the root is the only
network master node which maintains the routing information,
the root node is responsible for diverting the traffic from
source to destinations. It contains capabilities to loop
detection and DODAG repair and control traffic flows in RPL
technology. [51,52]
IEEE 802.15.4
The IEEE 802.15.4 protocol: Low Rate Wireless Personal
Network (LR –WPANS) is created for thelocaland
metropolitan network. This standard specifies sub-layer for
Medium Access Layer and Physical Layer which provides
connectivity for low power consumption and zero battery
wireless portable devices. In this standard devices are
operative on three frequencies 868–868.6 MHz, 902–928
MHz, and 2400–2483.5 MHz bands. This standard provides
the low cost, low data rate and high throughput of messages.
The main objective of this standard is providing the easiness of
installation, reliable transfer of data, low cost and flexibility to
maintain this standard. It supports two type of network a full-
function device (FFD) and reduced function device. First
fullfunction device (FFD), in which a device serves as
coordinator for PAN. On the other hand, (RFD) is quite simple
in which no device serves as Coordinator. It is proposed for
applications that are simple and not need to send a large
amount of data at atime. This standard operated on two
topologies: Star topology and peer to peer topology. In
astartopology, all devices are communicated with a special
device Coordinator of PAN. This device has anassociation
with theapplication and acts as initiation point or termination
for PAN. The Peer-to-peer topology is a complex network
implementation. It is mesh network which allows multiple
hopes. Any hope can communicate to any hope in the network.
It has also a coordinator device and acts as the first device in
the network. The further network structure is constructed in
peer-to-peer shape.[52]
Bluetooth Low Energy -BLE
BLE is also known as Bluetooth Smart in the communication
industry. This standard is an improved version of Bluetooth
technology and developed by Nokia’s project named “Wibree”
in 2006. It operates on the short range radio frequency which
covers range up 50(160 feet) meters. The key feature of this
technology is low energy consumption between 0.01 mW to
10 mW. Now this improved version is merged in main
Bluetooth with name Bluetooth 4.0. Many Smart Phone
Makers have adopted this technology and are using their Smart
Phone products. This standard can also be used in avehicle to
vehicle communication. It is much better than ZigBee because
it is more efficient in thematter of low energy consumption
and transmission bit ratio. The key difference from
theprevious version of Bluetooth technology is low power
consumption, 128-bit AES with counter mode CBC-MAC
level Securityand less latency rate 6ms. In the previous
versions, they consume more battery power for a huge amount
of data between two devices. This technology is not using
streaming and consumes low power battery than previous
versions. The architecture of BLE is divided among the three
section controller, Host, and Application in Figure 3. In the
controller part, the lower part is Physical Layer(PHY) which is
responsible for transmitting and receiving bits from asecond
Bluetooth device. Above the PHY, Link layer is responsible
for providing servicesof medium access layer like
theestablishment of connections, errordetection, and
correction, flow control etc.
Direct Test mode is used for end –product qualification. It
provides the standard and procedures for testing packets. It
operates in two modes: Transmit test mode and Receiving
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Test Mode.First Transmit mode tests packets which are
generated for transmission. Second Receive Test Mode is
testing received apacket from PHY. It also counts a numberof
received packets are in specific order. Host Controller
Interface (HCI) provides the Interface between controller and
Host. The Logical Link Control and
AdaptationProtocolprovide services fragmentation and
defragmentation of large size packets and
Figure 3 Architecture of BLE Stack
multiplexing for data communication channels. The attribute
protocol is used for efficient data collection from the sensors.
Generic Access Profile (GAP) is used for configuring
thedevice in the different mode scanning or
advertising,initiation, and management of connection. Security
Manager is the three-phase process on the connection: pairing
feature exchange, short term key generation, and transport
specific key distribution. It uses a number of cryptographic
function to secure the packets in the communication. It uses
lower memory and power requirement for encryption and
decryption process. As the result, it saves power.
Figure 4 BLE Star-Bus Topology
BLE use the Star –Bus Topology, like Bluetooth
technologyalso a master device thatcontrols the entire network.
Each slave acts piconet in topology. The slaves create anown
physical channel to communicate with the master node. These
all-physical channels of slave node are connected with master
node device and create star network. BLE master has fewer
power constraints for listening advertisement and making
theconnection between master-slave nodes. BLE master uses
these channels for scan slave nodes. After making
theconnection and data transfer between master and slave
node, they are going to sleep mode. [52]
Z- Wave
Z-wave is a low power wireless communication protocol for
smart homes and medium size commercial units. Danish of
Zen-Sys develop this protocol. Later this protocol is acquired
by Sigma Design in 2008. It covers communication range
about 30 meters between two nodes. It provides transmission
speed up to 100kbits for small packets with reliable and low
latency transfer rate. It operates on different frequencies in
different countries like India 865.2 MHz, 868.42 MHz in
Europe. New versions provide support up to 200 Kbits for
small packets.
Figure 5. Z-Wave Protocol Stack
Z-wave protocol includes five layers: Physical Layer (PHY),
Medium Access Layer (MAC), Transport Layer, Network
Layer and Application Layer. The Physical Layer takes
responsibility of modulation and coding of the message. It also
adds a known pattern as preamble with amessage. It also takes
responsibility for assignment of Radio frequency between two
nodes at adatarate of 9.6/40/100 Kps speed.The Z-Wave MAC
layercontrols medium with collision detection and avoidance
algorithms. It supports automatic retransmission of reliable
data transmission and 232 nodes in the one network.The
TransportLayer is responsible for transmission and reception
of frames in the network. It also takes care for retransmission
of frames. It supports four types of frames: Singlecast frame,
ACK Frame, Multicast Frame and Broadcast Frame. There
are controller and slave two types of node in the network. The
Network layer takes the responsibility find thebest route from
one node to another node. The controller sends commands for
managing the slave nodes in the network. It also keeps routing
of thewhole topology. The Application layer is responsible for
controlling payload of receiving and transmitting frames in the
network. It also responsible fordecoding of receiving frames
and execute commands in thenetwork. [52]
LTA –A
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
78
Long Term Evolution (LTE –A) is an improved version LTE
Standard. It includes cellular communication protocols, which
provide the higher of data transmission.It increased peak data
rate is down link 3 Gbps, Up Link 1.5 Gbps. The Carrier
aggregation is the new functionality introduced in LTE –A. It
is also suitable for IOT infrastructure, especially for smart
cities projects.Long Term Evolution (LTE –A) is an improved
version LTE Standard. It includes cellular communication
protocols, which provide the higher of data transmission. It
increased peak data rate is down link 3 Gbps, Up Link 1.5
Gbps. LTE-A is highest data rate speed and comparison of
data speed is given TableIV
the Carrier aggregation is the new functionality introduced in
LTE –A. It is also suitable for IOT infrastructure, especially
for smart cities projects. Figure 6 shows LTE-A Advance
Protocol Stack. This protocol stack has two classifications
namely Non-Access Stratum (NAS) and Access Stratum (AS).
Further NAS is also divided into two parts viz. Control Plane
and User Plane. Both Planes side have Four layers but control
plane has a special layer named RRC Layer. PHY (Physical
Layer) frame formation as per TDD or FDD Topology and
asper OFDMA based structure. MAC Layer is responsible for
Multiplexing/de multiplexing of RLC Packet Data Units
(PDUs), scheduling information reporting. [52]
Error correction through Hybrid ARQ (HARQ), Padding and
Local Channel Prioritization.
Figure 6 LTE-A Protocol Stack
Radio Link Control (RLC) is responsible for error correction
through Automatic Repeat request (ARQ). It provides services
like segmentation according to the size of the transport block
and re-segmentation in case a retransmission is needed,
Concatenation of SDUs for the same radio bearer, Protocol err
or detection and recovery and in-sequence delivery. Packet
Data Convergence Protocol (PDCP) provides services like
header compression. Duplicate detection, Ciphering and
integrity protection. Radio Resource Control(RRC) is
broadcast system information which is related to Non-Access
Stratum (NAS) and Access Stratum (AS). It is basically
responsible for theestablishment, maintenance, and release of
RRC connection. It also includes thefunctionalityfor mobility
and QoS management. It also takes care of -NAS direct
message transfer between UE and NAS.Non-Access Stratum
(NAS) is responsible for connection/session management
between UE and the core network.
EPCglobal
EPCglobal is an organizational set which is developing
standards for EPC and RIFD. These standards will be
acceptable in worldwide. This architecture is acceptable
because it is a promising technologyfor future IOT. With this
architecture, all vendors EPC device can communicate each
other. It creates a network viz. EPCglobal Network. Further,
this network is divided into five components: EPC, EPC
middleware, Discovery services,IDsystem,and EPC
Information Services.
Table IV
Comparison Considerationbetween Infrastructural
Protocols
TABLE III[54]
Comparison of data Speed LTE-A and others technologies
3G WiMAX HSPA+
LTE LTE
Advanced
Peak rate 3
Mbps
128
Mbps
168
Mbps
300
Mbps
1 Gbps
Download
rate
(actual)
0.5 –
1.5
Mbps
2 – 6
Mbps
1 – 10
Mbps
10 –
100
Mbps
100 – 300
Mbps
Upload
rate
(actual)
0.2 –
0.5
Mbps
1 – 2
Mbps
0.5 –
4.5
Mbps
5 –
50
Mbps
10 – 70
Mbps
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
79
Infr
astr
uct
ura
l
Pro
toco
l
Sp
read
ing
Tec
hn
iqu
e
Rad
io B
and
MA
C
Acc
ess
Dat
a
IEEE
802.15.4
DSSS 868/9
15/24
00
TDMA,
CSMA/
CA
20/40/
250 K
BLE FHSS 2400 TDMA 1024 K
EPC
global
DS-
DCMA
868-
960
ALOHA 5
K
LTE-A Multiple
CC
Varie
s
OFDMA 1G(
500 M
(down)
Z-Wave - 868/9
08/24
00
CSMA/
CA
40 K
B. Service Discovery protocols
The domain of IOT is highly scalable. Due to high scalability,
it is frequent to add and remove IOT devices
requires a resource management mechanism that is able to
register and discover the things in the network. The services
must be dynamic, self – configured, efficient and dynamic to
add and remove the devices and update the network
information. For this purpose, service discovery protocols are
used. There is two service discovery protocols are Multicast
DNS(mDNS), DNS Service discovery (DNS-SD).
1.1 Multicast DNS(mDNS)
Multicast DNS(mDNS) is a zero configuration service of
DNS for asmall network. It does not include a local name
server in thesmall network. It issuch service which can
perform thetask of unicast DNS. In this, DNS name space
resides in local space without bearingextra configuration
expense. mDNS is a suitable choice for Internet based IOT
devices. The main features of mDNSare 1) No need to manual
configuration 2). It is able to run service with infrastructure 3)
it is continuing to provide service even if
infrastructure has happened. This protocol is
by using an IP multicast message with UDP.
needs to resolve thename in the small network, i
multicast request to all node in the network. This message
query asks to all devices. The name is matched with its name.
If thenameis matched with hostname, then host send IP
address to receiving end. Each node holds network
configuration file inside thehost. It works fine in
Dat
a
Rat
e
Sca
lab
ilit
y
20/40/
250 K
65 K
1024 K 5917
5-640
K
-
1G(P)
500 M
(down)
-
40 K 232
nodes
. Due to high scalability,
IOT devices in real time. It
requires a resource management mechanism that is able to
and discover the things in the network. The services
configured, efficient and dynamic to
add and remove the devices and update the network
information. For this purpose, service discovery protocols are
cols are Multicast
SD).[52]
zero configuration service of
network. It does not include a local name
service which can
of unicast DNS. In this, DNS name space
extra configuration and
for Internet based IOT
1) No need to manual
service with infrastructure 3)
it is continuing to provide service even if thefailure of
resolving names
UDP. If any client
in the small network, it sends an IP
multicast request to all node in the network. This message
query asks to all devices. The name is matched with its name.
with hostname, then host send IP
address to receiving end. Each node holds network
. It works fine in thesmall
network if mDNS is working with “. local” extension
thename is not found. Thelocal file
name server for this purpose. [52]
1.2 DNS Service Discovery (DNS-
This protocol provides amechanism
network like printer service, mail servic
This service discovery mechanism
Discovery(DNS-SD). The clients can
network with coordination of mDNS
is Zero configuration to connect machines. There are two steps
to be taken to perform thejob of DNS
find host names of required services.
pairing the hostname with IP address. In this
the hostname is important because IP address
nature. IP address changes periodically.
message asks to host for IP address and the hostname will be
attached with IP address and port Id.
C. Application Protocols
ConstrainedApplication Protocol (CoAP)
ConstrainedApplication Protocol (CoAP) is a web transfer
protocol, which is used with constraints nodes,
network in IOT. This protocol is designed for Machine
Machine (M2M) applications. It works on
with 10 kb RAM and 100 kb code space.
protocol is toenable tiny
computationcapacity, and communication with
technology. This protocol is classified in two sublayers: the
messaging sublayerand Request/response
sublayer is responsible for detecting duplications and
providing reliable communication by using UDP. But UDP
does not support in built error recovery mechanism. This job is
done by REST technique. To fulfilled
it uses four types of messages confirmable message, non
confirmable message, rest message and
message. This protocol four type of modes of response
messages:confirmable response message, Non
messages, piggyback responses and
messages. COAP includes some of
Resource observation, block wise resource
discovery, interacting with HTTP and security
Message Queue Telemetry Protocol MQTT
MQTT is an application layer
lightweight messaging protocol.
Figure 7 MQTT on TCP Protocol
It works on top of TCP protocol.
protocol is to connect the embedded devices. It also connects
with “. local” extension file. If
file then it uses the unicast
-SD)
mechanism to discover service in the
network like printer service, mail service, GPS service etc.
This service discovery mechanism is known as DNS Service
SD). The clients can discover a service in the
of mDNS. Like mDNS, DNS-SD
configuration to connect machines. There are two steps
of DNS-SD.The first step is to
ired services. The second step is
pairing the hostname with IP address. In this technique finding
the hostname is important because IP address is dynamic
periodically. Multicast query
to host for IP address and the hostname will be
attached with IP address and port Id.[52]
Protocol (CoAP)
Protocol (CoAP) is a web transfer
protocol, which is used with constraints nodes, and constraints
network in IOT. This protocol is designed for Machine-to-
Machine (M2M) applications. It works on amicrocontroller
with 10 kb RAM and 100 kb code space. The aim of this
devices with power,
communication with based on REST
This protocol is classified in two sublayers: the
response sub layer. Messaging
for detecting duplications and
reliable communication by using UDP. But UDP
in built error recovery mechanism. This job is
fulfilled reliable communication,
it uses four types of messages confirmable message, non-
confirmable message, rest message and acknowledgment
This protocol four type of modes of response
message, Non-confirmable
responses and separate response
messages. COAP includes some of thefeatures, whichare
wise resource transport, resource
nteracting with HTTP and security.[52]
Message Queue Telemetry Protocol MQTT
MQTT is an application layer protocol, which uses as
MQTT on TCP Protocol
It works on top of TCP protocol. The basic aim of this
the embedded devices. It also connects
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
80
tiny devices to network with applications. It is designed for
aremote connection where the limited bandwidth is available.
It delivers messages through three modes: MQTT 3.1, MQTT-
SN and third mode is specially designed for sensor networks.
It uses a publish/ subscribebe pattern for Machine-to-Machine
communication. [52]
Figure 8: Communication pattern between Publisher, Broker, and Subscriber
It contains three components: Subscriber, publisher,and
broker. The subscriberis responsible for receiving amessage
from clients. The publisher is responsible for sending
messages from the clients in the form of Publish/ Subscribe
pattern. The broker is found in the middle between Subscriber
and Publisher. The broker provides services to Publisher for
sending amessage to Subscriber. It is an ideal messaging
protocol for communication between M2M and IOT. It also
provides routing facility for small, low battery and low
memory devices. It contains verities of messages as
CONNECT (Client request to request to server), CONNACK
(Connect Acknowledgement), PUBLISH (Publish
Message),PUBACK (Publish Acknowledgement),PUBREC
(Publish Received), PUBREL (Publish release), PUNCOMP
(Publish Complete), SUBSCRIBE (Client Subscribe Request),
SUBACK (Subscribe acknowledgment), UNSUBSCRIBE
(Unsubscribe Request),UNSUBACK (Unsubscribe
Acknowledgement)and DISCONNECT (Client is
disconnecting). The format of MQTT contains fields: Message
Type, UDP, QOSLevel, Retain, remaining Length are acore
part of themessage format. Variable Length and Variable
Length Message Payload are two optional fields in MQTT
message Format.[52] .Figure 9 shows MQTT message format.
Figure 9: MQTT Message Format
MQTT-SN
MQTT- SN is a special light weighted protocol, which is
design for Wireless Sensor Networks. It is running on the top
of ZigBee APS layer. It is optimal for sensor and wireless
devices, which consist small, low cost and low batteries.
Figure 10 MQTT-SN Architecture
This architecture includestheircomponents: MQTT-SN Client,
MQTT-SN Gateway, MQTT broker. MQTT-SN client is tiny
sensor device that can directly connect the network. It requires
converting the message into network format. MQTT-Client
approaches MQTT-SN Gateway for sending amessage to
thenetwork. MQTT-SN attaches with MQTT Broker. MQTT-
Broker sends amessage to Subscriber with using the Internet.
[52]
Extensible Messaging and Presence Protocol – (XMPP)
XMPP is an instant message protocol, which is useful for
Multi-user chatting, video calling, file transfer and IOT
devices commutation. It was design and developed by Jabber
communities in 1999. It is an open source protocol. This
protocol allows users communicate with each other with
Instant message mechanism.Figure 11 shows XMPP
architecture.
Figure 11 XMPP Architecture
This architecture includes three components XMPP gateway,
XMPP-Server, and XMPP-Client.XMPP isa client-server
architecture. XXPPworks as SMTP protocol and uses text
messages for communications. XXPP-Client communicates its
server. Other devices like SMS client cannot communicate
directly. SMS client can communicate with its server. Then
SMS-Server communicatesXXPP-Gateway, which provides
interaction service to XXPP protocol. Due to open protocol,
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
81
there is no centralized Server in the Globe. For this reason,
each organization createsown XXPP-Server. Each component
has aunique address. Address of each component is similar to
email id. The format of theaddress is “node @
domain/terminal”. It also provides the security, authentication
and encryption services to its. Due to text message format, it
is limited to do binary communication.
Advance Message Queuing Protocol-AMQP
AMQP is an open standard business messaging protocol. It
guarantees reliable communication by using message delivery
mechanism. This provides facility like message Orientation,
routing, queuing, security and reliability. For providing
reliable delivery of messages, it uses reliable protocol TCP for
exchanges messages. There are mainly two components handle
communication namely Exchange and message queues. The
main responsibility of exchange is to route the messages to
their queues. The messages are stored in the queues. This
protocol uses publish/subscribe model for communication. It
contains two types of message. [52]
Data Distribution Service –DDS
Data Distribution Service is known as middleware for
Machine-to-Machine communication in IOT. It operates on a
Publish-Subscribe pattern, which is developed by Object
Management Group (OMG). The main feature of this service
is broker fewer communications. It uses multicasting
broadcasting for communication between sender and receivers.
Its architecture contains mainly two layers: thefirst layer is
Data-Centric Publish-Subscribe Layer and responsible for
delivering messages to receivers. Another Layer is Data Local
Reconstruction Layer and responsible for providing interface
first layer. DDS Service contains five entities in its
architecture 1) Publisher 2) Data Writer 3) Subscriber 4) Data
Reader and 5) Topic. It is exclusively data centric middleware
service, which is a best for IOT applications. It provides
controlled, secure and manages service for IOT data
communication. [52]
V IOT PLATFORMS
Before discussing asurvey of IOT Platform, it is essential
firstly define the term Platform. The platform is a thing which
allows us to deploy and execute our applications. A platform
can be a combination of Hardware and Software bundles
which allow executing other applications. The platform is the
upper layer of theoperating system while operating is also
above the hardware. The platformprovides an environment to
execute easily without interacting directly with theoperating
system. IOT Platform provides an environment which
provides application-independent functionalities. These
functionalities could be used for application development. It is
also known as avirtual solution. It means information is
collected from thesmart object and send to another object is
known as data. The main objective of IOT Platform is
translating smart things data to useful information. There are
at least 50 IOT platform exit in the Global market. These IOT
platforms fulfill the requirement of different domain people
like healthcare, transportation, agriculture, government, and
manufacturing units. This module presents different IOT
Platforms to serve the different functionalities to adifferent
domain of IOT. The comparison study of different IOT
Platform is shown in Table V
TABLE V
IOT Platform Features [52,53]
Platform
Su
pp
ort
of
het
ero
gy
no
us
Dev
ices
Ty
pe
Arc
hit
ectu
re
Da
ta A
cces
s C
on
tro
l
Ser
vic
e D
isco
ver
y
RE
ST
Co
AP
XM
PP
MQ
TT
AirVantage ^ % C L - + - - +
Axeda + % C F - + - - -
ARM
mbed
E % C U - C
*
+ - -
Carriots + P C S - + - - + Device
Control
+ P C - - N - -- -
Everyware ^ P C N No + - - -
Evry Thing + % C F
G
- + - -- -
Exosite + P C N NO + - - -
Fosstrack R S* C* L No - - - -
Nimbits + S* C* 3
L
No + - + -
Ninja
Platform
^ P C O NO + - - -
RealTime.io ^ P C S
A
O + - - -
Sensor
Cloud
N
O
P C N
o
NO + - - -
Tempo DB N
O
P C N
O
No + - - -
ThingWorx + % C N
O
+ + - - +
Xively + P C O
S
+ + - - +
^ Need Gateway, % M2M PaaS, C- Cloud Based, L-Library
only, +Yes, -No, O- OAuth2, E-Embedded Devices, U-User
choice. F-Facebook, C*- CoAP,P-PaaS, S-Squire access,N-
Not Applicable, FG-Fine Grain, R-RIFD, S*-SERVER, C*-
Centralized, L-Locally stored, 3L-three level, OS-Open Source
[52-60]
VI RESEARCH CHALLENGES AND FUTURE TRENDS
A. Research Challenges of IOT
.
There are some technical challenges of IOT is listed below
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
82
1. Designing a service oriented architecture of IOT
2. Handlingof complicated heterogeneous Network
3. Lack of common standard for commutations and
Interoperability of IOT Platform and IOT device
4. Use of traditional ICT technology.
5. Resource discovery, resources management, data
management, event management and code management
are challenges still exist in IOT Middleware
6. RPL suffers from internal attacks and
Issues.
7. There are many security issues in Network layer of
I6LoWPAN.
8. IOT Virtualization is limitations for implementing IOT
things properly.
9. New feature and technologies of IOT cannot
previous Security protocols, whichare
Internet.
10. High availability of theresource is also a
IOT.
11. Reliability is achallenge in IOT; the system must be
working as per specification. Here, Hardware and
software must work properly as a
eachLayer of IOT.
12. Mobility is a challenge of IOT. In IOT,
mobile users. Providing most of theservices
users is abig challenge in IOT.
13. Evaluation of performance of IOT services
challenge. There are so component and
adding, developing andimproving every
challenging task to evaluate the performance whole IOT.
14. Scalability is achallenge for IOT. It must provide
theability to add new services, devices
without affecting previous active services.
be unaware of these changes.
15. Security is an important research challenge
It is difficult to provide securityguarantee
standardization and architecture of IOT devices.
B. Survey Result & Future Trends.
The study of larger number papersprovides an
IOT domain. This study provides the search results about
different research areas and research possibilities, which
existing in IOT. The survey shows what trends are going on
beyond 2015. The Table VI shows the number of papers
hasstudied by authors and trends are shown in figure 10. At
the end Table,VII provides alistof IOT Research domains
research possibilities variables.
TABLE VI
Survey Trends of IOT
IOT Domain Sub Domain No of Articles
study
Survey papers 22
Healthcare 18
IOT Platform 15
Designing a service oriented architecture of IOT
Network
commutations and
Interoperability of IOT Platform and IOT device
Resource discovery, resources management, data
management and code management
Middleware.
rom internal attacks and Performance
in Network layer of
IOT Virtualization is limitations for implementing IOT
New feature and technologies of IOT cannot secure by
are based on the
abig challenge in
the system must be
, Hardware and
aspecification in
IOT, almost users are
services to mobile
IOT services is a
ent and services are
developing andimproving every day. It is
evaluate the performance whole IOT.
It must provide
devices, and functions
previous active services. The user must
challenge for the IOT.
securityguarantee due to wear
standardization and architecture of IOT devices.
provides an overview of
IOT domain. This study provides the search results about
possibilities, which are
IOT. The survey shows what trends are going on
the number of papers
and trends are shown in figure 10. At
Research domains and
o of Articles
study
IOT Routing
IOT Software
IOT Master
Thesis
IOT Ph.D.Thesis
Networking
Security Issue
Smart Society
Smart City
Smart Devices
Smart Grid
Smart home
Smart industry
Smart Tourism
Web of Things
Architecture
M2M
Cloud & IOT
Fog & IOT
RIFD
Total Research Papers Studied
Figure 10 IOT Research Areas and Future Trends
TABLE VII
IOT Research Areas and Future beyond 2015
Research Domains Research Needs & possibilities
Hardware Devices Polymer based memory, Molecular
Sensors, Biodegrade antennas
Autonomous
Communication Self-configuring, Protocol seamless
15%
7%
6%
5%
2%2%
22%
IOT Research Trends
Survey papers Healthcare
IOT Platform IOT Routing
IOT Software IOT master thesis
IOT PhD thesis Networking
Security issue
07
06
05
02
02
22
17
10
10
6
4
5
5
10
15
10
5
4
200
IOT Research Areas and Future Trends
IOT Research Areas and Future beyond 2015
Research Needs & possibilities
Polymer based memory, Molecular
Sensors, Biodegrade antennas,
Autonomous devices
configuring, Protocol seamless
23%
18%
IOT Research Trends
Healthcare
IOT Routing
IOT master thesis
Networking
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
83
Technologies Network
Network
Technologies
Need Based Network
Software and
Algorithms
Context aware, Self- Reusable,
Self-configurable, Self –Healing,
Self-Management, Self-generating,
Platform for Object Intelligence
Power and Energy
Storage Technologies
Paper based Batteries, Power
generation for harsh environment
Security and privacy Context based Security algorithms,
Cognitive Security systems, Service
triggered Security, Object
Intelligence
Standardization Dynamic Standards, Evolutionary
Standards,
Standards of Interacting devices
and Personalized devices
Healthcare IOT Smart Rehabilitation, Smart Patient
Monitoring, Smart Hospital
management, Smart Human
Resource Monitoring, Smart
Medicine ATMs, Wound Analysis
for diabetes patients, Wheelchair
management, Cough detection,
Oxygen saturation monitoring
Smart Cities Smart Traffic Monitoring, Smart
Building, Smart Water Supply
System, Smart Roads and Public
Infrastructures
Smart Tourism Smart Tourist places monitoring,
Tourist guidance GPS, Smart
Hotels monitoring, Smart Tourist
Vehicles monitoring
Smart Grids New time series forecasting
methods,communications
infrastructure for self-healing grids,
enhanced reliability and power
quality studies,
improvementinpower
flowoptimization,new EV battery
techniques to prolong their useful
life,practical methods for large
scale RES integration, cloud based
control and management,new and
improved battery systems,battery
wearing in V2G
Smart Industries Smart Machines,Smart
Devices,Smart Manufacturing
Processes, Smart Engineering,
Manufacturing IT, Smart
logistics,Factory visibility and
optimized decision-making,Remote
monitoring,Proactive maintenance
Smart Suppliers
Fog & IOT Latency Constraints, Network
Constraints, Resource Constraints
Devices, Uninterrupted Services
connectivity to Cloud, Security
technique
Mobile Phone with
IOT
Heterogeneity, Continue Sensing,
Crowd Sensing, Persuasion, Search
and Discovery
RPL Recovery mechanism form
falsification attacks, Byzantine
attacks, Selective –forward attacks,
Sinkhole attacks, gray hole attacks,
black hole attacks, version number
manipulation attacks, Routing
information Replay, Secure
connection, and authentication
mechanism
Routing Self-stabilization, Location Privacy,
Light weight Computations, secure
routing protocols for Tiny devices,
Effective node identification
system
VII CONCLUSION
This survey article is the summary of thecurrent state of IOT
research. It provides aglance on research domains existing in
the IOT by examining 200 literature surveys. This paper
identifies current trends andinnovations, which are helpful in
describing the challenges existing in IOT. It also keeps the
promise of improving thelives of thelife of people. The
potential shown in this paper can prove to save people and
money. It can be helpful for an organization to improve
decision and outcomes in the wide range of research area in
IOT. Different technologieslike WSN, RFID, EPC,
Middleware etc. have been overviewed in this paper. The
application of IOT domain trees like smart city, smart home,
smart tourism, smart industries, and healthcare gives aview of
asmart network of smart objects for providing meaningful
information for people. It also describes asurvey of IOT
platforms, which provides different features of different IOT
Platform. A number of Challenges arefocused on the overall
picture of IOT domains, which highlights possible research
opportunities for future IOT Researchers as a whole.
VIII REFERENCES
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[2] Jayavardhana Gubbia, Rajkumar Buyyab, Slaven Marusic,
Marimuthu Palaniswami, “Internet of Things (IoT): A vision,
architectural elements, and future directions”, Future
Generation Computer Systems 29 (2013) 1645–1660.
[3]. EPC Information http://www.epc-rfid.info/
[4]. Jennifer Yick, Biswanath Mukherjee, Dipak Ghosal,
“Wireless sensor network survey”,* Department of Computer
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States.
[5]. Shancang Li & Li Da Xu & Shanshan Zhao, “The internet
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9492-7.
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Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Proceedings of First International Conference on Computational Intelligence and Communication Technologies
87
STRATEGIES IN HYBRID
EVOLUTIONARY ALGORITHMS FOR
OPTIMIZATION
VISHAL JAIN
Associate Professor
Dept. of EE, BMIET, Sonepat
Dr. DEVENDER SAINI
Assistant Proffesor
Dept. of EE, UPES, Dehradun
SALONI
Assistant Professor
Dept. of EEE, BMIET, Sonepat
ABSTRACT--Evolutionary computing has grown to
be a significant methodology in the field of research.
Robustness and adaptation are some of the prime
features of evolutionary algorithms as compared to
other global optimization techniques. Even though
evolutionary computation is popularly used for solving
several important practical problems in engineering,
business, commerce, etc., there is scope for fine-tuning
its performance. It is not easy to find any one best
algorithm for solving all optimization problems. Hence
there is need for a hybrid algorithm which is capable of
handling several real world challenges such as, noise,
imprecision and uncertainty. This paper presents a
review on the methodologies adopted for hybrid
evolutionary Algorithms.
Keywords--Evolutionary algorithms,Genetic
Algorithm,Particle Swarm Optimization,Ant Colony
Optimization,Bacterial Foraging Optimization
I.INTRODUCTION
Evolutionary Computation, offers multiple advantages for difficult optimization problems including, the effortlessness of the approach, its adaptability, and numerous different aspects [1-4]. Recently the evolutionary algorithms have been vastly used, particularly for practical problem solving. Generally the term evolutionary computation or algorithms are used for the domains of genetic algorithms, and genetic programming [5]. These techniques use the concept of evolution through the basic three operators namely selection, mutation, and crossover. As Compared to other optimization techniques, EA produce good solutions and also are easy to implement. Fig 1 illustrates a basic structure of EA
A detailed survey on evolutionary computational
algorithm reveals that, for many problems a direct
evolutionary algorithm does not produce an optimal
solution [6-9]. This clearly gives a way for the need
for hybridization of evolutionary algorithms with
different optimization algorithms and heuristic
techniques.
Fig.1. Structure of Evolutionary Algorithm
II. REASONS FOR HYBRIDIZATION:
The efficient use of hybridization results in the
following improvement [10]:
1. It improves the quality of Solution obtained
2. It improves the convergence speed of the
Algorithm
3. It incorporates the EA as part of a larger system
III. HYBRID STRUCTURES FOR EA
Fig.2 illustrates the opportunity for incorporation of
other techniques. Population may be initialized by
introducing known solutions. Local search methods
may be incorporated within the initial population
members or among the off-springs. EA may be
hybridized by using distinct operators from same or
other algorithms by incorporating domain
knowledge.
PARENT
OFFSPRING
POPULATI
ON
SELECTI
ON
REPRODUCTI
ON REPLACEM
ENT
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88
Fig 2 Hybridization possibilities in EA
Some of most utilized hybrid structures are outlined
as below:
1. ANN based EA
2. Fuzzy Logic based EA
3 PSO based EA
4. ACO based EA
5. Bacterial foraging optimization (BFO) based EA
6 Other heuristic techniques such as SA, Tabu search,
dynamic programming, hill climbing, etc. combined
with EA.
A Artificial Neural Networks Based Ea
The ANN techniques [11] are used for enhancing the performance of evolutionary algorithms. Neural networks (NN) are constructed based on the collected training samples. EA are then used to search good solutions. After the generation of new solutions, the fitness function is determined by NN. However multiple NN’s may be used to provide statistical predicted performance for the evolutionary algorithm.
B Flc Based Ea
Fuzzy logic controller (FLC) is based on the expert knowledge of the system and can be effectively used to frame linguistic control rules and provide a fuzzification interface by means of reasoning. It effectively transforms the crisp data into fuzzy sets. FLCs have been used to design adaptive evolutionary algorithms. Fuzzy Logic Controllers have been efficiently utilized to tune GA parameters [12]. The inputs of the FLC are chosen as different GA performance measures, and outputs may be considered as GA control parameters.
C Pso Based Ea
The concept of PSO originated from the swarming patterns observed in bees, birds or schools of fish, and even human social behavior [13-14]. A variety of hybrid evolutionary algorithm and PSO approaches are proposed in the literature [15]. The hybrid technique implements the two structures simultaneously and chooses P population from every framework for trading after the assigned N cycles.
The individual with superior fitness traits has more probability of getting selected.
A GA and PSO hybrid technique [16] is used to decipher optimization problem. In this method, in each cycle, the population is split into two parts and subsequently evolved with the two techniques, respectively. The two populations are then recombined in the fresh population, that is again divided randomly into two parts in the next iteration for another run of genetic or particle swarm operators.
D Aco Based Ea ACO approach is mimicked from the foraging
behavior of real ants, which are utilized to solve distinct optimization problems [17]. Various hybrid permutations have been used with ACO such as GA, Local search and Tabu Search. The hybridization introduces unique immigration scheme, new crossover schemes and random heuristics which enhances the diversity and helps in promoting the optimization process.
E Bacterial Foraging Optimisation Based Ea
Recent research shows that bacteria have been instrumental in solving optimization problems [18]. The foraging nature of Escherichia coli bacteria is mimicked. The various steps involve chemotaxis, swarming, reproduction, elimination and dispersal. Hybrid genetic algorithm (HGA) [19] and bacterial foraging approach has been used for optimization. In the hybrid structure, the members of population may be considered as a cluster of bacteria foraging in the problem search space. It is evident from the results that the hybrid GA-BFO approach is superior to a direct GA approach.
IV.CONCLUSIONS
Literature reveals that, the hybrid evolutionary
algorithms is gaining immense popularity and is
widely used for handling optimization problems. In
this paper, an attempt has been made to represent the
various strategies for hybridization of an evolutionary
algorithm and also to discuss its advantages over
direct EA. Some of the common hybrid frameworks
reported in the literature is also presented.
REFRENCES
1. Deb, K. 2002. Multi-objective optimization usingevolutionary algorithms Ross, S., & Weber, R., Eds.2nd ed. John Wiley and Sons Ltd.
2. D. E. Goldberg, Genetic Algorithms in Search,Optimization and Machine Learning. Reading, MA:AddisonWesley, 1989.
3. J.P. Rosca and D. H. Ballard, “Learning by adaptingrepresentations in genetic programming,” in Proc. 1stConf. Evolutionary Computation, 1994, pp. 407–412.
4. J. Kennedy, R. Eberhart, and Y. Shi, Swarm Intelligence,1st ed. San Mateo, CA: Morgan Kaufmann, 2001.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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5. Koza JR (1992) Genetic Programming, MIT Press,Cambridge, MA
6. Li F, Morgan R, and Williams D (1997) Hybrid geneticapproaches to ramping rate constrained dynamiceconomic dispatch, Electric Power Systems Research, 43(11),
pp. 97–103 7. Lo CC and Chang WH (2000) A multiobjective hybrid
genetic algorithm for the capacitatedMulti-point network design problem, IEEE Transactions on
Systems, Man and Cybernetics - Part B, 30(3), pp. 461–470
8. Somasundaram P, Lakshmiramanan R, and KuppusamyK (2005) Hybrid algorithm based on EP and LP forsecurity constrained economic dispatch problem, Electric Power Systems Research, 76(1–3), pp. 77–85
9. Tseng LY and Liang SC (2005) A hybrid metaheuristicfor the quadratic assignment problem, ComputationalOptimization and Applications, 34(1), pp. 85–113
10. Sinha A and Goldberg DE (2003) A Survey of hybridgenetic and evolutionary algorithms, ILLIGALTechnical Report 2003004
11. Wang L (2005) A hybrid genetic algorithm-neuralnetwork strategy for simulation optimization, AppliedMathematics and Computation, 170(2), pp. 1329–1343
12. Lee MA and Takagi H (1993) Dynamic control ofgenetic algorithms using fuzzy logic techniques, InForrest S (Ed.), Proceedings of the 5th InternationalConference on Genetic algorithms, MorganKaufmmann, San Mateo, pp 76–83
13. Kennedy J and Eberhart RC (1995) Particle swarmoptimization, In Proceedings of IEEE InternationalConference on Neural Networks, Perth, Australia, pp.1942–1948
14. Kennedy J (1997) The particle swarm: social adaptationof knowledge, In Proceedings of IEEE InternationalConference on Evolutionary Computation,Indianapolis, IN, 1997, pp. 303–308
15. Shi XH, Liang YC, Lee HP, Lu C, and Wang LM(2005) An improved GA and a novel PSO-GA-basedhybrid algorithm, Information Processing Letters,93(5), pp. 255–261
16. Grimaldi EA, Grimacia F, Mussetta M, Pirinoli P, andZich RE (2004) A new hybrid genetical – swarmalgorithm for electromagnetic optimization, InProceedings of International Conference onComputational Electromagnetics and its Applications,Beijing, China, pp. 157–160
17. Dozier G, Bowen J, and Homaifar A (1998) Solvingconstraint satisfaction problems using hybridevolutionary search, IEEE Transactions onEvolutionary Computation, 2(1), pp. 23–33
18. Passino KM (2002) Biomimicry of bacterial foragingfor distributed optimization and control, IEEE ControlSystems Magazine, 22(3), pp. 52–67
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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Home and Automobile Automation Model
1, Gurminder Kaur,
2 Priyansh Gupta 1Assistant professor B.M. Institute of Engg. & Tech., Sonepat
2B.Tech, CSE , 4TH Year, BMIET Sonepat
Abstract— Wave lash (Home automation expert) is a
mobile web based application that permits its user to control
their home, automobile and monitor both using their
Smartphone. System requires a micro SD card with an OS for
the Raspberry Pi (Raspbian OS/NOOBS OS).Security is one of
the most critical component for its users who use Wave lash for
their home and automobile convenience and protection. For
security the system uses OTP (One Time Password) generation
which will be used as entry password for user. A messaging
API is used which helps in sending text messages to the user
about any activity in house, with the help of a text to speech
plug-in user can record messages which can be played when he
is not at home. Data from all these sensors is continuously
received and processed by Arduino Uno board that finctions as
a microcontroller unit. In case of unexpected situations, the
Arduino will trigger an alarm and alert messages will be sent
to user’s mobile via GSM. Thus the current system ensures
home and automobile safety as well as security with
incorporation of automation.
Keywords—Automation, Embedded System, GPIO Pins,
IOT, RFID
I. INTRODUCTION
In this fast growing world with new technologies emerging every day, Our lives have become much more dependent on electronic gadgets. In today’s world almost all electronic devices may be controlled with a remote wirelessly why should not there be a remote that provides us control over home but as well as over automobiles. Smart phones now being the basic necessity of our lifestyle can be used to bridge the gap and could give us the ability to control these
II. WHAT EXACTLY IS IOT?
Internet of things or IOT can be described as an ecosystem of technologies which communicates through IP networks with the software applications by monitoring the status of physical objects and collects meaningful data from them. Internet of things has evolved immensely with the help of multiple technologies including wireless sensors, real time analytics, and control Systems. It includes cars, machines in production plants, jet engines, oil drills, wearable devices, and more. These “things” collect and exchange data. Theembedded technology in IOT helps them to interact with
internal states or the external environment, which in turn affects the decisions taken.
III. TECHNOLOGIES USED WITH IOT
It incorporates some necessary components that enable the communication between various devices and objects. Each object is augmented with an Auto-ID tag or RFID tag so that the device may be easily identified giving it a unique id. It also allows objects to communicate wirelessly.
IV. HOW IOT CAN HELP
When devices can represent themselves digitally, they can be controlled from anywhere.IOT Platforms can help reduce the cost of organizations by reducing the cost and improving the efficiency and boosting the production, security. With improved tracking of devices with the help of sensors can help in acquiring real time insights and analytics which could help in making smarter decisions.
V. NEED OF IOT
The connectivity then helps us capture more data from more places, ensuring more ways of increasing efficiency and improving safety and IoT security. As objects are reporting real time data, we as users have the ability to make quicker and more accurate decisions for example in connected car with IoT comes in a revolutionary way for us to drive and stay in touch with the user and enabling Real Time Location of the car to provide security features as well. Home Automation means controlling various in-home devices like AC, television automobiles and other electrical devices over a wireless network connection.
VI. CONCEPT REVIEW
These requirements will allow the user to gain knowledge in how he should design the overall system so that it
functions as per the requirements stated below.
• Controlling Light Devices
• Controlling Ventilation Devices
• Bluetooth Connectivity
• Wi-Fi Connectivity
• Detecting Smoke and Gas Leaks
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• Detecting Unauthorized Entry into House
• Keyless Entry house and Vehicle.
• Voice Integration & Motion
• Finding Current Temperature and Humidity
• Implementing Augmented Reality
• User’s Room Location Detection
• Google Maps API to calculate ETA to Home.
• Motion/Activity Module.
• Car security services.
• Vehicle location tracking and scheduling
solutions.
• SOS
• Smart alerts and notifications
• Connected vehicle sensors
Figure 1 IOT Connectivity Basic Illustration
Advantages of proposed System:
The new system must provide the following features:
• It allows more flexibility through android device.
• It allows a good range of scalability.
• It provides security and authentication.
• Additional vendors can be easily added.
V. SYSTEM DESIGN
The Basic Level DFD diagram for the proposed project that
we would be using is
Figure 2 Data Flow Diagram
System design the flow of the user with sensor works as
follows:
1) The user requests the specific task that he needs to
perform.
2) Following request is then transmitted to the designed
system .
3) then it transfers the query request to the server of Google
Firebase.
4) It then responds back to the hardware with the
corresponding task assigned to it.
5) The embedded system receives the signal changes and
then shows back the physical change with respect to
users query.
VI. SOFTWARE DEVELOPMENT LIFE CYCLE MODEL
The model we would be using would be V-model
Figure 3 Software Development life Cycle(SDLC)
VII. System Interfaces
The system is intended to interact with one single user at a time. The user will be able to interact easily with Graphical User Interface. The android application interacts with the user and simultaneously communicates to the hardware implemented in the appliances through either the Bluetooth or the Wi-Fi technology and retrieves the data in real time and displays it on the user’s phone.
User Interfaces
The user interface provides an ease of access to application.
• Home Activity has a Constraint layout which
contains the user image, the current temperature
and carousal containing the different rooms.
• Each carousal contains some appliances and setting
of one room.
• The google maps activity marks the home of the
user
• The bottom sheet in each room lists the appliances
and refresh button
VIII. Hardware Requirements
i) Electronics
• Arduino UNO
• Raspberry Pi-3 Model B
• PIR sensors
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• Smoke sensor (MQ 2)
• LDR sensor
• DHT-11 sensor
• ESP8266 Wi-Fi module
• Solderless Breadboard Half Size
• Eight Channel 12V Relay Board
• Resistor(s) 10k ohm• LEDs
• Jumper Cables
• Sample Phone
IX. Software Requirements
i) Electronics:• Python IDE
• Arduino IDE
ii) Android:• Android Studio IDE
• Android Studio SDK
X. Conclusion
This project provides In-House Appliance Control and security along with Automobile IOT (Vehicle Communication) which makes living comfortable and at the same time easily accessible through smartphones. It provides user almost all rights to decide which makes it reliable as it always asks before taking a decision, which helps when there are necessary decisions to be taken and they can be taken fast in case of an emergency.
XI. Future Scope
The Raspberry Pi being a really compact processor which has excellent computing power for its size. With daily research and development in technologies and various portable devices in those devices may be one day the raspberry might also be used as it has multiple GPIO pins which can be programmed and used to interface various devices in the real world and can be controlled with a program in Python.
XII. REFRENCES
1. https://www.irjet.net/archives/V3/i3/IRJET-V3I3133.pdf2. https://www.irjet.net/archives/V3/i4/IRJET-V3I4265.pdf3. http://inpressco.com/wp-content/uploads/2016/05/Paper1.750-
754.pdf 4. http://www.ijrat.org/downloads/ncpci2016/ncpci-45.pdf 5. http://www.jncet.org/Manuscripts%5CVolume-6%5CIssue-
4%5CVol-6-issue-4-M-05.pdf 6. http://ijitech.org/uploads/623514IJIT8573-11.pdf 7. https://www.kaaproject.org/automotive/ 8. https://blog.atlasrfidstore.com/internet-of-things-and-rfid9. AmulJadhav, S. Anand, NileshDhangare, K.S. Wagh “Universal
Mobile Application Development (UMAD) On Home Automation” https://www.arduino.cc/en/main/howto
10. Gayatri Kulkarni, Priyanka Gode, JadiPratap Reddy and MadhuraDeshmukh (2015), Android based smart home system, International Journal of Current Engineering and Technology, vol. 5, pp. 1022-1025. Pierre Raufast (2013), Raspberry Pi Open
CV and camera board, https://thinkrpi.wordpress.com/opencv-andpicamera-board/
11. Lady ada (2014), PIR motion sensors, Pyroelectric (Passive) InfraRed Sensors, https://learn.adafruit.com/pir-passiveinfrared-proximity-motion-sensor/overview
12. Rakesh Ron (2013), L293D Motor Driver IC, http://www.rakeshmondal.info/l293d-motor driver. Jason Barnett (2014), Controlling DC Motors Using Python with a Raspberry Pi, http://computers.tutsplus.com/tutorials/controlling-dcmotors-using-python-with-a-raspberry-pi--cms-20051
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
93
A New Variant of bat algorithm and Clustering
Approach for optimization problems
Savita Khatri
Department of Computer Science and Engineering, BMIET,
Sonepat, India
Neeraj Dahiya
Department of Computer Science and Engineering,
SRM University, Delhi-NCR Campus, India
Abstract— this paper proposes a new variant of bat algorithm
for optimization problems for solving global and continuous
optimization problems to expand the weakness of BAT. The
planned algorithm uses the genetic bat tactics for improving the
search and to overcome the deficiency, directional echolocation is
introduced to the standard bat algorithm to enhance its
exploration and exploitation capabilities. The outcomes are taken
on six benchmark optimization problems. From upshots, it is
detected that the planned algorithm delivers more augmented
outcomes in contrast to same class of algorithms. The ordinary
test outcomes display the supremacy of the turning bat
algorithm.
Keywords— Bio-inspired algorithm, Optimization, exploration
and exploitation and echolocation
I. INTRODUCTION
Optimization can be an active research area for several decades and provides robust and viable solutions for complex real-world optimizations problems. These problems are complex and take lot of efforts to find optimal solution duet to increasing dimensionality, differentiability, multi-modality and rotation characteristics. So, lot of research has been carried out in this direction to design a real-time numerical optimizer for developing more accurate, fast and computationally efficient optimization algorithms. Clustering is an unsupervised technique which can be applied to understand the organization of data. The basic principle of clustering is to partition a set of objects into a set of clusters such that the objects within a cluster share more similar characteristics in comparison to the other clusters. A pre-specified criterion has been used to measure the similarity between the objects. In clustering, there is no need to train the data, it only deals with the internal structure of data and used a similarity criterion to group the objects into different clusters. Due to this, it is also known as unsupervised classification technique. Clustering has proven its importance in many applications successfully
In literature, Some of our Population-based algorithms, such as differential evolution (DE), evolutionary strategies (ES), genetic algorithm (GA), and particle swarm optimization (PSO), have been extensively used to solve such problems Researchers have proposed various techniques to recover these defective elements method [1], conjugate gradient based method [2, 3], applying genetic algorithm (GA) [4, 5], with the hybridization of GA and fast fourier transform (FFT) [6], applying an adaptive neuronal system [7], with simulated
annealing (SA) [8, 9], particle swarm optimization (PSO) [10–12], and firefly algorithm (FA) [13, 14].
The rest of paper is organized as follows: the section 2 describes the related work in field of bat algorithm. Section 3 gives the introduction of bat algorithm. The proposed new variant of BAT algorithm is illustrated in section 4. Section 5 describes the results of proposed BAT algorithm using benchmark optimization functions and the whole work is concluded into section 6.
II. RELATED WORK
The standard bat algorithm has been proven to be a very powerful optimization tool and Dao et al. (Dao, Pan, Nguyen, Chu, & Shieh, 2014) developed a compact version of the bat algorithm (CoBA), addressing to the hardware devices with limited resources such as the memory size or low price equipment. The bat population is replaced with a probability vector updated based on a single computation. These lead to an algorithm functioning with a modest memory usage. Results show that the CBSO performances are as good as the standard BA despite its modest memory usage. The enhanced bat algorithm (EnBA) proposed in (Yilmaz & Küçüksille, 2015) has been developed through three different methods. An inertia factor has been proposed to balance the search capabilities during the optimization process depending on the requirement of BA. Experimental results reveal that the proposed improvements make the BAT algorithm more effective and significant one for solving global optimization problems. To avoid the premature convergence and preserve the population diversity especially for global optimization problems, Zou et al., have described an improved BAT algorithm based on dynamic group strategy [21]. Further, it is also found that a quantum behaved learning scheme is also induced into learner phase of BAT algorithm for helping to maintain the population diversity. The feasibility of the proposed algorithm is evaluated on eighteen benchmark numerical functions and results reveal that the proposed algorithm is one of the effective and efficient algorithms for solving global optimization problems. The performance of BAT is investigated on twenty benchmark functions and it is found that the BATexhibits better performance than other algorithm being compared.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
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III. BAT ALGORITHM
The standard Bat algorithm is inspired by the echolocation process of bats. By noticing the conduct and features of the micro-bats, Yang (2010) planned the standard Bat in agreement to three main features of the echolocation process of the micro-bats. The recycled venerated rules in BAT are:
Algorithm 1: The standard bat algorithm.
Step1: Define the objective function
Step2: Initialize the bat population
Step3: Define frequencies
Step4: Initialize and loudness
Step5: While (pulse rate ≤ pulse rate max)
Step6: Adjust frequency and Update velocities
Step7: Update locations/solutions
Step8: Select a solution among the best solutions
Step9: Generate a local solution around the selected best
solution
Step10: Generate a new solution by flying randomly
Step11: Accept the new solutions
Step12: Rank the bats and find the current best
Step13: Output results for post-processing
IV. PROPOSED TLBO ALGORITHM
The detailed description of proposed BAT algorithm is given in Algorithm 2. The main steps of proposed algorithm are summarized as below.
V. RESULTS.
This section describes the results of proposed BAT algorithm
with well-known global optimization problems. The proposed
algorithm is implemented in Matlab 2010 (a) environment
using window based operating system having core i7 processor
and 8 GB RAM. The results are taken on average of 90
independent runs and evaluated using mean and standard
deviation parameters. The mean parameter shows the
efficiency of algorithms, whereas standard deviation parameter
defines robustness of the algorithm. The performance of
proposed algorithm is compared with some other algorithms
such as PSO, GA and BAT Table 1 depicts the various
unimodal and multi-modal problems used for experimentation.
TABLE I. THE USED TEST FUNCTIONS FOR EXPERIMENTATION
Table 2 demonstrates the results of proposed algorithm and
other algorithm like PSO, GA, bat and proposed bat. The
performance of the proposed algorithm is measures against
the six well-defined global optimization functions. These
functions are widely adopted to check the performance of
newly developed algorithms The experimental results reveal
that the proposed algorithm provides more optimized and
better results in comparison to existing algorithm. Figs. 1 and
2 show the convergence pattern of proposed bat and original
bat algorithm using Ackley and step functions. From these, it
is also stated that the convergence of the bat algorithm is
significantly improved.
Proceedings of First International Conference on Computational Intelligence and Communication Technologies
95
TABLE II. COMPARISON OF RESULTS OF PROPOSED BAT AND OTHER EXISTING ALGORITHM ON DIFFERENT NUMERICAL FUNCTIONS
Algorithm Parameter
Function
F1 F2 F3 F4 F5 F6
PSO average 0.0004 26.8437 1.3582 0.1045 0.0721 0.0005
std. 0.0025 17.2699 1.6975 0.0562 0.0506 0.0028
GA average 0.8378 46.2842 1.9000 0.9613 0.89934 0.7879
std. 0.6138 22.6284 1.6921 0.0548 0.3216 0.5645
BAT average 01.0000 27.6567 1.97E−12 0.0098 4.55E−16 2.76E−89
std. 01.0000 2.94E−02 7.66E−12 0.0090 9.32E−32 5.38E−09
Proposed
BAT
average 0.0000 23.9648
1.0000 0.0099 4.39
E−15 3.58E−26
std. 0.0000 3.26E−01 0.0000 0.0000 7.53E−21 5.99E−26
Fig. 1. shows the BAT and Proposed BAT algorithm for Ackley function
Fig. 2. shows the convergence of BAT and Proposed BAT algorithm for Step function
0 50 100 150 200 250 300 10 -14
10 -12
10 -10
10 -8
10 -6
10 -4
10 -2
10 0
10 2
Iteration
Cos
t
Ackley
Proposed BAT
BAT
0 50 100 150 200 250 300 0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5 x 10
5
Iteration
Cost
Step
Proposed BAT
BAT
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96
VI. CONCLUSION
In this work, a new variant of BAT algorithm is presented for
solving the global optimization problems. Various operators is
adopted to improve the searching capability and convergence
rate of BAT algorithm. In this work, six benchmark global
optimization functions are used to evaluate the performance of
the proposed algorithm. The results depict that the proposed
BAT algorithm obtains better performance among all other
being compared and produced quality results.
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