1. INTRODUCTION Wireless Sensor Networks, with the characteristics of low energy consumption, low cost, distributed and self organization, have brought a revolution to the information perception. The wireless sensor network is composed of hundreds of thousands of the sensor nodes that can sense conditions of surrounding environment such as illumination, humidity, and temperature. Each sensor node collects data such as illumination, humidity, and temperature of the area. Each sensor node is deployed and transmits data to base station (BS). The wireless sensor network can be applied to variable fields. For example, the wireless sensor network can be used to monitor at the hostile environments for the use of military applications, to detect forest fires for prevention of disasters, or to study the phenomenon of the typhoon for a variety of academic purposes. These sensor nodes can self organize to form a network and can communicate with each other using their wireless interfaces. Energy efficient self organization and initialization protocols are developed in, [2]. Each node has transmitted power control and an Omni directional antenna, and therefore can adjust the area of coverage with its wireless transmission. Typically, sensor nodes collect audio, seismic, and other types of data and collaborate to perform a high-level task in a sensor web. For M. Tech (ACS), NIT Warangal Page 1
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1. INTRODUCTION
Wireless Sensor Networks, with the characteristics of low energy consumption, low cost,
distributed and self organization, have brought a revolution to the information perception.
The wireless sensor network is composed of hundreds of thousands of the sensor nodes
that can sense conditions of surrounding environment such as illumination, humidity, and
temperature. Each sensor node collects data such as illumination, humidity, and
temperature of the area. Each sensor node is deployed and transmits data to base station
(BS). The wireless sensor network can be applied to variable fields. For example, the
wireless sensor network can be used to monitor at the hostile environments for the use of
military applications, to detect forest fires for prevention of disasters, or to study the
phenomenon of the typhoon for a variety of academic purposes. These sensor nodes can
self organize to form a network and can communicate with each other using their wireless
interfaces. Energy efficient self organization and initialization protocols are developed in,
[2]. Each node has transmitted power control and an Omni directional antenna, and
therefore can adjust the area of coverage with its wireless transmission. Typically, sensor
nodes collect audio, seismic, and other types of data and collaborate to perform a high-level
task in a sensor web. For example, a sensor network can be used for detecting the presence
of potential threats in a military conflict. Most of battery energy is consumed by receiving
and transmitting data. If all sensor nodes transmit data directly to the BS, the furthest node
from BS will die early. On the other hand, among sensor nodes transmitting data through
multiple hops, node closest to the BS tends to die early, leaving some network areas
completely unmonitored and causing network partition. In order to maximize the lifetime of
WSN, it is necessary for communication protocols to prolong sensor nodes’ lifetime by
minimizing transmission energy consumption, sending data via paths that can avoid sensor
nodes with low energy and minimizing the total transmission power.
M. Tech (ACS), NIT Warangal Page 1
Figure 1.1 A typical Wireless Sensor Network.
Figure 1.2: Schematic of a Wireless Sensor Network Architecture
M. Tech (ACS), NIT Warangal Page 2
1.1 Architecture of Wireless Sensor Network:
Figure 1.2 shows a typical schematic of a wireless sensor network (WSN). After the initial
deployment (typically ad hoc), sensor nodes are responsible for self-organizing an
appropriate network infrastructure, often with multi-hop connections between sensor
nodes [30]. The onboard sensors then start collecting acoustic, seismic, infrared or magnetic
information about the environment, using either continuous or event driven working
modes. Location and positioning information can also be obtained through the global
positioning system (GPS) or local positioning algorithms. This information can be gathered
from across the network and appropriately processed to construct a global view of the
monitoring phenomena or objects. The basic philosophy behind WSNs is that, while the
capability of each individual sensor node is limited, the aggregate power of the entire
network is sufficient for the required mission.
In general, the wireless sensor networks are deployed for monitoring at a large area so the
wireless sensor networks need many sensor nodes. If the sensor node consumes completely
energy, it is wasted. We do not consider to recharge and to reuse sensor node. Because of
these reasons, the value of the sensor nodes must be inexpensive to practical use. Deployed
in harsh and complicated environments, the sensor nodes are difficult to recharge or
replace once their energy is drained. Meanwhile the sensor nodes have limited
communication capacity and computing power. So how to optimize the communication
path, improve the energy-efficiency as well as load balance and prolong the network
lifetime has became an important issue of designing routing protocols for WSN.
Hierarchical-based routing protocols [6] are widely used for their high energy-efficiency and
good expandability. The basic idea of them is to select some nodes in charge of a certain
region routing. These selected nodes have greater responsibility relative to other nodes
which leads to the incompletely equal relationship between sensor nodes. LEACH (Low
Energy Adaptive Clustering Hierarchy) [7], PEGASIS (Power-Efficient Gathering in Sensor
Information System) [8] are the typical hierarchical-based routing protocols. As an
enhancement algorithm of LEACH, PEGASIS is a classical chain-based routing protocol. It
saves significant energy compared with the LEACH protocol by improving the cluster
configuration and the delivery method of sensing data.
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1.2 The differences between WSNs and traditional networks
Wireless sensor networks, on the one hand, share the similarity of self-configuration
without manual management with Mobile ad-hoc networks; on the other hand, they are
different from traditional networks in many aspects due to their strict energy constraints
and application specific characteristics.
NO one-size-fits-all solution: A WSN is organized as a collection of sensor nodes which co-
ordinate with each other to fulfil a certain task. The entire network infrastructure depends
directly on the specific application scenario. It is unlikely that a one-size-fits-all solution
exists for all these different applications. The old fixed protocol stack which applied
successfully to traditional networks is no longer suitable for WSNs. Many new
communication algorithms have been developed for different applications. As one example,
WSNs are deployed with very different network densities, from sparse to dense
deployments. Each case requires unique network configuration.
Environment interaction: The traffic loads relayed in WSNs are generated by the sensors
which interact entirely with the environment. By contrast, the traffic loads of tradition
network are mainly driven by human behaviour. Moreover, the environment plays a key
role in determining the size of the network, the deployment scheme, and the network
topology. The size of the network varies with the monitored environment. For indoor
environments, fewer nodes are required
to form a network in a limited space whereas outdoor environments may require more
nodes to cover a larger area.
Resource constraints: Resource constraints include a limited amount of energy, short
communication range, low bandwidth, and limited processing and storage in each node. For
wireless sensor networks, energy is a scare resource. This is unlike wireless ad-hoc networks
which can recharge or replace batteries quite easily. In some cases, the need to prolong the
lifetime of a sensor node has a deep impact on the entire WSN system architecture.
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Reliability and QoS: The WSNs exhibit very different concepts of reliability and quality of
Service from traditional networks. They totally depend on the task assigned. In some
emergency cases, only occasional delivery of packets can be more than enough; in other
cases, very high reliability requirements exist. Packet delivery ratio in WSNs is no longer an
sufficient metric, instead, different applications may take their own requirements into
consideration.
1.3Design challenges:
WSNs distinguish themselves from traditional networks due to their application specific and
energy constraints. Their structure and characteristics depend on their electronic,
mechanical and communication limitations but also on application specific requirements.
One of the major and probably most important challenges in the design of WSNs is their
application specific characteristic. A sensor network is set up to fulfil a specific task and the
data collected from the network may be of different types due to various application
scenarios. Respectively, different types of applications have their own specific requirements.
These requirements are turned into specific design properties of a WSN. In other words, a
WSN's architecture directly depends on the assigned application scenarios. For the
acceptable performance of a given task, the optimal WSN infrastructure should be selected
out of the hundreds of network solutions before the practical deployment.
Equally, an issue that has been frequently emphasized in the research literature is the fact
that energy resources are significantly limited. Recharging or replacing the battery of sensor
nodes may be difficult or impossible. Hence, power efficiency often turns out to be the
major performance metric, directly influencing the network lifetime. Power consumption
according to the functioning of a sensor node can be divided into three domains: sensing,
communication, and data processing. There has been research effort in hardware
improvements to optimize the energy consumed by sensing and data processing. Several
studies of energy efficiency of WSNs have been discussed and several algorithms that lead
to optimal connectivity topologies for power conservation have been proposed [10][18].
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Another issue in the design of WSNs is that performance assessment of a WSN always
happens once deployed. The analysis procedure follows the order that people in this field
first put more and more effort into inventing new protocols and new applications; then the
solutions are built, tested and evaluated either by simulation or test beds; even sometimes
an actual system has to be deployed so that researchers can learn by empirical evidence. A
more scientific analysis procedure is ideally required before a WSN is practically deployed.
Current WSN designers are mainly experts in wireless sensor networking and hardware who
could perceive the communication between each node at the bit level. When a new
protocol is developed, they could construct algorithms even if the required simulation tool
did not exist. As WSNs immerse deeper into people's work, they must begin to include less
specialized users.
1.4 Thesis Contributions
The work reported herein investigates chaining mechanism in PEGASIS using evolutionary
algorithms like Ant Colony optimisation and Genetic algorithms and lifetime enhancement
by chain leader selection criteria and maintenance of priority queue at each node if the next
node fails. Lifetime measurement of WSN using various types of PEGASIS variants for both
Homogenous and heterogeneous has been evaluated.
1.5 Thesis Outline
The thesis has been organised in the fallowing manner. Fallowing this chapter, chapter 2
presents extensive literature survey on routing algorithms for WSN. It mainly discusses
energy efficient hierarchical routing protocols for WSN. Evolutionary algorithms are also
presented in this section. Chain forming mechanism using GREEDY algorithm is presented in
chapter 3. It mainly investigates the lifetime of PEGASIS protocol under various scenarios.
Chapter 4 deals with Ant Colony Optimisation technique applied to PEGASIS protocol and
lifetime Measurement. Chapter 5 deals with Genetic algorithm and its lifetime
measurement. Chapter 6 gives the comparative study of all the algorithms proposed.
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2 LITERATURE SURVEY OF ROUTING PROTOCOLS FOR WSN
2.1 Introduction
Wireless sensor networks have their own unique characteristics which create new
challenges for the design of routing protocols for these networks. First, sensors are very
limited in transmission power, computational capacities, storage capacity and most of all, in
energy. Thus, the operating and networking protocol must be kept much simpler as
compared to other ad hoc networks. Second, due to the large number of application
scenarios for WSN, it is unlikely that there will be a one-thing-fits-all solution for these
potentially very different possibilities. The design of a sensor network routing protocol
changes with application requirements. For example, the challenging problem of low-
latency precision tactical surveillance is different from that required for a periodic weather-
monitoring task. Thirdly, data traffic in WSN has significant redundancy since data is
probably collected by many sensors based on a common phenomenon. Such redundancy
needs to be exploited by the routing protocols to improve energy and bandwidth utilization.
Fourth, in many of the initial application scenarios, most nodes in WSN were generally
stationary after deployment. However, in recent development, sensor nodes are
increasingly allowed to move and change their location to monitor mobile events, which
results in unpredictable and frequent topological changes [10].
Due to such different characteristics, many new protocols have been proposed to solve the
routing problems in WSN. These routing mechanisms have taken into consideration the
inherent features of WSN, along with the application and architecture requirements. To
minimize energy consumption, routing techniques proposed in the literature for WSN
employ some well-known ad hoc routing tactics, as well as, tactics special to WSN, such as
data aggregation and in-network processing, clustering, different node role assignment and
data-centric methods. In the following sections, introduction to current research on routing
protocols has been presented.
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2.2 Routing Challenges and Design Issues in WSNs:
Despite plethora of applications of WSN, these networks have several restrictions, e.g.,
limited energy supply, limited computing power, and limited bandwidth of the wireless links
connecting sensor nodes. One of the main design goals of WSN is to carry out data
communication while trying to prolong the lifetime of the network and prevent connectivity
degradation by employing aggressive energy management techniques. In order to design an
efficient routing protocol, several challenging factors should be addressed meticulously. The
following factors are discussed below:
Node deployment: Node deployment in WSN is application dependent and affects the
performance of the routing protocol. The deployment can be either deterministic or
randomized. In deterministic deployment, the sensors are manually placed and data is
routed through pre-determined paths; but in random node deployment, the sensor nodes
are scattered randomly creating an infrastructure in an ad hoc manner. Hence, random
deployment raises several issues as coverage, optimal clustering etc. which need to be
addressed.
Energy consumption without losing accuracy: sensor nodes can use up their limited supply
of energy performing computations and transmitting information in a wireless environment.
As such, energy conserving forms of communication and computation are essential. Sensor
node lifetime shows a strong dependence on the battery lifetime. In a multi hop WSN, each
node plays a dual role as data sender and data router. The malfunctioning of some sensor
nodes due to power failure can cause significant topological changes and might require
rerouting of packets and reorganization of the network.
Node/Link Heterogeneity: Some applications of sensor networks might require a diverse
mixture of sensor nodes with different types and capabilities to be deployed. Data from
different sensors, can be generated at different rates, network can follow different data
reporting models and can be subjected to different quality of service constraints. Such a
heterogeneous environment makes routing more complex.
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Fault Tolerance: Some sensor nodes may fail or be blocked due to lack of power, physical
damage, or environmental interference. The failure of sensor nodes should not affect the
overall task of the sensor network. If many nodes fail, MAC and routing protocols must
accommodate formation of new links and routes to the data collection base stations. This
may require actively adjusting transmit powers and signalling rates on the existing links to
reduce energy consumption, or rerouting packets through regions of the network where
more energy is available. Therefore, multiple levels of redundancy may be needed in a fault-
tolerant sensor network.
Scalability: The number of sensor nodes deployed in the sensing area may be in the order of
hundreds or thousands, or more. Any routing scheme must be able to work with this huge
number of sensor nodes. In addition, sensor network routing protocols should be scalable
enough to respond to events in the environment. Until an event occurs, most of the sensors
can remain in the sleep state, with data from the few remaining sensors providing a coarse
quality.
Network Dynamics: Most of the network architectures assume that sensor nodes are
stationary. However, mobility of both BS‘s and sensor nodes is sometimes necessary in
many applications. Routing messages from or to moving nodes is more challenging since
route stability becomes an important issue, besides energy, bandwidth etc. Moreover, the
sensed phenomenon can be either dynamic or static depending on the application, e.g., it is
dynamic in a target detection/tracking application, while it is static in forest monitoring for
early fire prevention. Monitoring static events allows the network to work in a reactive
mode, simply generating traffic when reporting. Dynamic events in most applications
require periodic reporting and consequently generate significant traffic to be routed to the
BS.
Transmission Media: In a multi-hop sensor network, communicating nodes are linked by a
wireless medium. The traditional problems associated with a wireless channel (e.g., fading,
high error rate) may also affect the operation of the sensor network. As the transmission
energy varies directly with the square of distance therefore a multi-hop network is suitable
M. Tech (ACS), NIT Warangal Page 9
for conserving energy. But a multi-hop network raises several issues regarding topology
management and media access control. One approach of MAC design for sensor networks is
to use CSMA-CA based protocols of IEEE 802.15.4 that conserve more energy compared to
contention based protocols like CSMA (e.g. IEEE 802.11). So, Zigbee which is based upon
IEEE 802.15.4 LWPAN technology is introduced to meet the challenges.
Connectivity: The connectivity of WSN depends on the radio coverage. If there exists a
multi-hop connection between any two nodes continuously, the network is connected. The
connectivity is intermittent if WSN is partitioned occasionally, and sporadic if the nodes are
only occasionally in the communication range of other nodes.
Coverage: The coverage of a WSN node means either sensing coverage or communication
coverage. Typically with radio communications, the communication coverage is significantly
larger than sensing coverage. For applications, the sensing coverage defines how to reliably
guarantee that an event can be detected. The coverage of a network is either sparse, if only
parts of the area of interest are covered or dense when the area is almost completely
covered. In case of a redundant coverage, multiple sensor nodes are in the same area.
Data Aggregation: Sensor nodes usually generate significant redundant data. So, to reduce
the number of transmission, similar packets from multiple nodes can be aggregated. Data
aggregation is the combination of data from different sources according to a certain
aggregation function, e.g., duplicate suppression, minima, maxima and average. It is
incorporated in routing protocols to reduce the amount of data coming from various
sources and thus to achieve energy efficiency. But it adds to the complexity and makes the
incorporation of security techniques in the protocol nearly impossible.
Data Reporting Model: Data sensing and reporting in WSNs is dependent on the application
and the time criticality of the data reporting. In wireless sensor networks data reporting can
be continuous, query-driven or event-driven. The data-delivery model affects the design of
network layer, e.g., continuous data reporting generates a huge amount of data therefore,
the routing protocol should be aware of data-aggregation
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Quality of Service: In some applications, data should be delivered within a certain period of
time from the moment it is sensed; otherwise the data will be useless. Therefore bounded
latency for data delivery is another condition for time-constrained applications. However, in
many applications, conservation of energy, which is directly related to network lifetime, is
considered relatively more important than the quality of data sent. As the energy gets
depleted, the network may be required to reduce the quality of the results in order to
reduce the energy dissipation in the nodes and hence lengthen the total network lifetime.
Hence, energy-aware routing protocols are required to capture this requirement.
2.3 Classification of Routing Protocols in WSNs:
In general, routing in WSNs can be divided into flat-based routing, hierarchical-based
routing, and location-based routing depending on the network structure. In flat-based
routing, all nodes are typically assigned equal roles or functionality. In hierarchical-based
routing, however, nodes will play different roles in the network. In location-based routing,
sensor nodes' positions are exploited to route data in the network.
A routing protocol is considered adaptive if certain system parameters can be controlled in
order to adapt to the current network conditions and available energy levels. Furthermore,
these protocols can be classified into multipath-based, query-based, negotiation-based,
QoS-based, or routing techniques depending on the protocol operation. In addition to the
above, routing protocols can be classified into three categories, namely, proactive, reactive,
and hybrid protocols depending on how the source sends a route to the destination. In
proactive protocols, all routes are computed before they are really needed, while in reactive
protocols, routes are computed on demand. Hybrid protocols use a combination of these
two ideas. When sensor nodes are static, it is preferable to have table driven routing
protocols rather than using reactive protocols. A significant amount of energy is used in
route discovery and setup of reactive protocols. Another class of routing protocols is called
the cooperative routing protocols. In cooperative routing, nodes send data to a central node
where data can be aggregated and may be subject to further processing, hence reducing
route cost in terms of energy usage.
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Figure 2.1 Classification of routing protocols
2.4 Previous Work:
In this section a brief review of the related work on the analysis of PEGASIS protocol is
presented. Cosmin Cirstea [10] provides an up to date evaluation of routing protocols as
well as a description of state of the art routing techniques for Wireless Sensor Networks
(WSNs) that enhance network lifetime through efficient energy consumption methods. The
tradeoffs between energy and communication overhead are studied. The advantages and
disadvantages of each routing protocol with the purpose of discovering new research
Figure 5.9 Lifetime of GENETIC Heterogeneous – Sequential cluster head
5.5.3 Comparison of Hetero Max and Sequential scenarios
It can be observed from the above figures that 10% of node die at around 2500 round for
Max Energy where as it is 2900 round for Sequential cluster head selection. 50% of nodes
die in Max Energy case at around 5200 and it is 5600 for sequential cluster head selection.
Max energy network is completely down at 7800 rounds whereas it is 10300 for Sequential.
M. Tech (ACS), NIT Warangal Page 68
Genetic HETERO Max Energy Sequential % improvement
10% 2500 2900 16
50% 5200 5600 7.7
100% 7800 10300 32
Table 6.2 Lifetime comparison of Max Energy and Sequential for Heterogeneous WSN
0 10 20 30 40 50 60 70 80 90 1000
2000
4000
6000
8000
10000
12000
number of dead nodes in percentage
num
ber
of
rounds
100comparision of GENETIC hetero max and sequence smooth
GENETIC hetero max lifetime
GENETIC hetero sequence lifetime
Figure 5.10 Lifetime Comparison of Hetero Max and Sequential
5.6 Conclusion:
Hence it can be concluded that for Genetic Algorithm for both Homogenous and
Heterogeneous sequential cluster head selection maximises the lifetime of Wireless Sensor
Network lifetime. In Genetic there is not much increase in improvement, since the length of
the chain is very high, so the distance between the node is playing dominant role rather
than the packet data size. But Genetic algorithm is very simple to understand and to
formulate the process.
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6. COMAPRATIVE STUDY OF GREEDY, ACO AND GENETIC ALGORITHMS
The chain formed using the Wireless Sensor Node is crucial in deciding the lifetime of the PEGASIS protocol. The length of the chain formed using Greedy, Ant Colony Optimisation and Genetic algorithm is compared in the fallowing figure.
0 20 40 60 80 100 120 140 160 180 200500
1000
1500
2000
2500
3000
3500
4000
4500
iteration number
leng
th in
met
ers
100comparision of chain length
GREEDY
Ant Colony OptimisationGENETIC ALGORIYHM
Figure 6.1: Chain length comparison of GREEDY, ACO and GENETIC Algorithm.
From the figure it can be seen that the chain formed using Ant Colony Optimisation is less compared to others.
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6.1 Lifetime Comparison of Homogenous WSN
Lifetime comparison of both MAX Energy and Sequential cluster head selection for GREEDY, ACO and GENETIC Algorithms is done.
6.1.1 Lifetime Comparison of MAX Energy
0 10 20 30 40 50 60 70 80 90 1000
1000
2000
3000
4000
5000
6000
number of dead nodes in percentage
num
ber
of r
ound
s
100comparision of HOMO max lifetime
GREEDY HOMO MAX lifetime
ACO HOMO MAX lifetimeGENETIC HOMO max
Figure 6.2 Lifetime Comparison of MAX Energy
From the above figure it can be concluded that the Genetic Algorithm lifetime is less compared to others mainly because of the chain length is very high compared to other algorithms. Few nodes in GREEDY algorithm die soon in the beginning itself but for ACO up to great extent of Network Lifetime hardly any dies.
M. Tech (ACS), NIT Warangal Page 71
6.1.2 Lifetime Comparison of Sequential Energy
0 10 20 30 40 50 60 70 80 90 1000
2000
4000
6000
8000
10000
12000
number of dead nodes in percentage
num
ber
of r
ound
s
100comparision of HOMO SEQUENTIAL lifetime
GREEDY HOMO SEQUENTIAL lifetime
ACO HOMO SEQUENTIAL lifetimeGENETIC HOMO SEQUENTIAL
Figure 6.3 Lifetime comparison of Sequential
From the above figure it can be concluded that the Genetic Algorithm lifetime is less compared to others mainly because of the chain length is very high compared to other algorithms. Few nodes in GREEDY algorithm die soon in the beginning itself but for ACO up to great extent of Network Lifetime hardly any node dies.
M. Tech (ACS), NIT Warangal Page 72
6.2 Lifetime Comparison of Heterogeneous WSN
Lifetime comparison of both MAX Energy and Sequential cluster head selection for GREEDY, ACO and GENETIC Algorithms is done.
6.2.1 Lifetime Comparison of MAX Energy
0 10 20 30 40 50 60 70 80 90 1000
1000
2000
3000
4000
5000
6000
7000
8000
9000
number of dead nodes in percentage
num
ber
of r
ound
s
100comparision of hetero max lifetime
GREEDY hetero MAX lifetime
ACO hetero MAX lifetimeGENETIC hetero max
Figure 6.4 Lifetime Comparison of MAX Energy
From the above figure it can be concluded that the Genetic Algorithm lifetime is less compared to others mainly because of the chain length is very high compared to other algorithms. Few nodes in GREEDY algorithm die soon in the beginning itself but for ACO up to great extent of Network Lifetime hardly any dies
From the above figure it can be concluded that the Genetic Algorithm lifetime is less compared to others mainly because of the chain length is very high compared to other algorithms. Few nodes in GREEDY algorithm die soon in the beginning itself but for ACO up to great extent of Network Lifetime hardly any dies
6.3 Conclusion
Chain formed by Ant Colony Optimisation is of least length compared to Greedy and Genetic Algorithm, because of good global optimisation characteristics of the Ant Colony Optimisation. The rate of convergence of Ant Colony optimisation is also very fast compared to other algorithms. The chain is optimised to a great extent within little iteration itself.
The WSN Lifetime is high in case of Sequential cluster head selection since the length of the data packet is less compared to Max Energy cluster head selection criteria.
M. Tech (ACS), NIT Warangal Page 74
7. CONCLUSION
7.1 Introduction
The research carried out for this thesis, investigates energy efficient routing algorithms
related to WSNs. A new cluster head selection criteria and maintenance of priority queue at
each node is proposed. This increases the life of WSNs. Ant Colony Optimisation and Genetic
Algorithms are used in making the chain of PEGASIS. Lifetime of WSN under various
scenarios has been investigated. These chapter summaries the work reported in this thesis,
specifying the limitations of the study and provides some suggestions to future work.
Following this introduction, section 7.2 lists the achievements of the research work. Section
7.3 presents some of the future research area that can be extended to this thesis.
7.2 Contribution of Thesis
The first chapter of the thesis introduced to Wireless Sensor Networks, literature survey and
its architecture. It also provides a brief overview of the thesis. The second chapter discussed
routing algorithms in Wireless Sensor Networks. It presented the literature survey of
PEGASIS routing protocol and of Evolutionary algorithms. The chapter 3 described the
PEGASIS protocol and chain formation using GREEDY algorithm. It also gave radio power
model of WSN and various simulation parameters. The chapter 4 described Ant Colony
Optimisation for minimisation of chain length in PEGASIS, thus contributing to the lifetime of
the WSN. The chapter 5 described Genetic Algorithm. The results of studies have been
presented. The chapter 6 presented the comparative study of GREEDY, ACO and Genetic
algorithms. The chain length and Lifetime of PEGSIS using GREEDY, ACO and Genetic
Algorithm has been compared.
The first contribution of the thesis related to use of sequential cluster head for PEGASIS by
eliminating the overhead, enhances the lifetime of the WSNs. Instead of sending the Energy
status of all the nodes to base station, the next cluster head is selected by the present chain
leader, thus contributing to higher lifetime of Wireless Sensor Network by reduced packet
M. Tech (ACS), NIT Warangal Page 75
size. To validate the algorithm Simulations had been carried out using MATLAB. Simulation
results showed better performance of Sequential as compared to MAX energy in terms of
performance metrics like number of alive nodes and total WSN lifetime.
ACO provides better lifetime for nodes compared to other models. It is also seen that ACO is
able to provide high percentage of nodes live for maximum duration. The chain length
formed ACO is least and converges to the optimum in very little iteration. Hence, it suits
most of application of WSNs which require constant monitoring and sending sensed data
packets to a sink at regular intervals of time.
7.3 Future Directions
To conclude the thesis, the following are some suggestions for the future work which can be
done. In this thesis, ACO and Genetic algorithms have been used. Other bio-inspired
algorithms like Stimulated Annealing, Bacterial Forage optimization, artificial Immune
system (significant time and power consuming) can also be compared to ACO and Genetic
algorithm, but the challenge of reducing computational complexity still remains.
Comparable study of computational complexity of different algorithm need to be analysed.
Secondly, security parameter has not been evaluated in this thesis. So, new security based routing
protocols for Wireless Sensor Networks and its validation can be a field of study. Further, the
proposed protocols have to be dumped into WSN nodes and can be tested in a real time
application.
M. Tech (ACS), NIT Warangal Page 76
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