The Proceedings

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Volume 1 & 2

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The ProceedingsOf 2nd National Conference on Innovation and Entrepreneurship in Information and Communication Technology May 14-15, 2011

Editor-in-Chief

Dr. Anil Kumar Pandey, Convener, SIG-IEICTEditors 1. Dr. Saba Hilal, GNIT-MCA Institute 2. Mr. Pradeep Agrawal, GGIT 3. Dr. S.K. Pandey, GGIT 4. Dr. Shikha Jalota, GGIT 5. Mr. Ankit Shrivastava, GGIT 6. Ms. Monika, GNIT-MCA Institute 7. Ms. Jyoti Guglani, GGIT 8. Mr. Amit Kumar, GGIT

Copyright 2011 All rights reserved. No part of this publication may be produced or transmitted in any form or by any means without the written permission of Special Interest Group on (IE-ICT)

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EDITORIALIndia is one of the youngest nations in the world. There are millions in the employable age. The conventional methods alone would not be adequate to address this problem. Innovation and entrepreneurship are key to job creation and national competitiveness. Technological advancements are increasing at rapid pace and developed economies are deriving economic dividends to create wealth and improve the efficiency of public services and processes. Throughout the developing world innovative entrepreneurs are working to establish businesses that are ICT enabled. India is at a threshold of new takeoff. The Indian IT industry is likely to clock revenues of over USD $70 billion by the end of this year and by USD $220 billion by 2020 that is why innovation and entrepreneurship and ability to combine the two in the domain of ICT becomes of immense importance. ICT has impacted our society in all walks of life education, employment, healthcare, communication, governance, business, banking to defense and disaster management. This has been possible due to innovative entrepreneurial venture that have come up using ICT and ICT enabled services. However in countries like India taking it to rural areas and integrating with agriculture, sanitation and village level micro enterprises are yet to be developed and commercialised. The university/ institute based innovators routinely produce breakthrough technologies that, if commercialized by industry, have the power to sustain the economic growth. However, this is not happening. In absence of this the business world is witnessing the rise of the student entrepreneurs who start their entrepreneurial journey at a very early stage while still pursuing their education. In order to help innovators and entrepreneurs create ventures academic institutions including schools, government, businesses and investors must work together. A clear knowledge of incubation support for taking their ideas as start up would help the economic growth as well as job opportunities for millions of youths of this country. This conference has been organized with the objective to bring students, faculty members, IT professionals, government agencies, venture capital agencies, innovation foundations and social entrepreneurs on a common platform to elicit and explore sources of innovation that exists among the upcoming and informal group of population who can be converted into the entrepreneurs of tomorrow through possible intervention generated here. It has the following aims. To provide a platform for students to meet and interact with innovators, successful entrepreneurs, government agencies and Venture Capitalists. To develop insight for integrating entrepreneurial experiences in the formal education process. To empower and sensitize students and faculty towards wealth creation through innovation and entrepreneurship. To give the basic knowledge and tools for setting up technology driven enterprise. To invoke drive and motivation to convert Intellectual Property Rights into enterprise through innovative management. Special Interest Group (SIG) on Innovation and Entrepreneurship in ICT, CSI Ghaziabad Chapter and Mahamaya Technical University have joined hands to organize this conference. It is a matter of great satisfaction that the response in terms of research papers as well as participation has been overwhelming and nation wide. Distinguished academicians, scholars from universities, government departments and entrepreneurs are participating to make it meaningful. We present here all the papers, PPTs and abstracts submitted by the authors. In future we intend to have third party review and get them published.

Dr. Anil Kumar Pandey Editor-In-Chief

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The ProceedingsEditorial

2nd National Conference on Innovation and Entrepreneurship in Information and Communication Technology, May 14-15, 2011

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ContentsVolume 1New Technology acceptance model to predict adoption of wireless technology in healthcare Manisha Yadav, Gaurav Singh, Shivani Rastogi.9 Solutions to Security and Privacy Issues in Mobile Social Networking Nikhat Parveen, Danish Usmani..15 Wireless Monitoring Of The Green House Using Atmega Based Monitoring System: WSN Approach Miss.Vrushali R.Deore., Prof. V.M. Umale..21 Fuzzy C- Mean Algorithm Using Different Variants Vikas Chaudhary, Kanika Garg, Arun Kr. Sharma.27 Security Issues In Data Mining Rajeev Kumar, Pushpendra Kumar Singh, Arvind Kannaujia ..38 Web Caching Proxy Services: Security and Privacy issues Mr. Anoop Singh , Mr. Rohit Singh, Ms. Sushma Sharma ..42 A Comparative Study to Solve Job Shop Scheduling Problem Using Genetic Algorithms and Neural Network Vikas Chaudhary, Kanika Garg, Balbir Singh..48 Innovation & Entrepreneurship In Information And Communication Technology Deepak Sharma Nirankar Sharma, Nimisha Srivastava .55 Insider Threat: A Potential Challenges For The Information Security Domain Abhishek Krishna, Santosh Kumar Smmarwar, JaiKumar Meena , Monark Bag, Vrijendra Singh ...57 Search Engine: Factors Influencing The Page Rank PrashantAhlawat, Hitesh Kumar Sharma..63 Are The Cmmi Process Areas Met By Lean Software Development? Jyoti Yadav68 Password Protected File Splitter And Merger (With Encryption And Decryption) Mrs. Shikha saxena, Mr. Rupesh kumar sharma .73 Security Solution in Wireless Sensor Network Pawan Kumar Goel , Bhawnesh Kumar ,Vinit Kumar Sharma..77 Vertical Perimeter Based Enhancement Of Streaming Application P.Manikandan, R.Kathiresan, Marie Stanislas Ashok82 Orthogonal Frequency Division Multiplexing for Wireless Communications Meena G shende.87 A Comprehensive Study of Adaptive Resonance Theory Vikas Chaudhary, Avinash Dwivedi, Sandeep Kumar, Monika Bhati..91 Is Wireless Network Purely Secure? Mrs Shikha Saxena, Mrs Neetika Sharma,Ms Rachana Singh..98 An innovative digital watermarking process A Critical Analysis Sangeeta Shukla..104 Design Of A Reconfigurable Sdr Transceivers Using Labview Sapna Suri, Vikram Verma, Rajni Raghuvanshi And Pooja Pathak.111 A Modified Zero Knowledge Identification Scheme Using ECC Kanika Garg, Dr. R. Radhakrishan, Vikas Chaudhary, Ankit Panwar116 Security and Privacy of Conserving Data in Information Technology Suresh Kumar Kashvap, Pooja Agrawal , Minakshi Agrawal Vikas Chandra Pandey 120 Barriers to Entrepreneurship - An analysis of Management students

5Dr. Pawan Kumar Dhiman ..126 A Novel Standby Leakage Power Reduction Method Using reverse Body Biasing Technique for Nanoscale VLSI Systems James Appollo .A.R, Tamijselvan. D..131 Survey On Decision Tree Algorithm Jyoti Shukla ,Shweta Rana ..136 Distributed Security Using Onion Routing Ashish T. Bhol , Savita H Lambole 141 Virtulization implementation in an Enterprise Rohit Goyal.146 Design of data link later using WiFi MAC protocols K.Srinivas (M.Tech) 149 Leveraging Innovation For Successful Entrepreneurship Dr. Sandeep kumar , Sweta Bakshi, Ankita Pratap ..153 Performance evaluation of cache replacement Algorithmd for Cluster Based cross layer design for Cooperative Caching (CBCC) in Mobile-Ad Hoc Network Madhavarao Boddu, Suresh joseph k..165 Owerment And Total Quality Management For Innovation And Success In Organisations Ms. Shamsi Sukumaran K , Ms. Bableen Kaur .178 A New Multiple Snapshot Alogorithm for Direction of Arrival Estimation using Smart Antenna Lokesh L , Sandesha karanth, Vinay T, Roopesh , Aaquib Nawaz ...185 Quality Metrics for TTCN -3 and Mobile Web Application Anu saxena , Kapil Saxena ...190 A Unique Pattern matching Algorithm Using The Prime Number Approach Nishtha kesswani , Bhawani Shankar Gurjar .....194 Study and implementation of Power control in Ad hoc networks Animesh srivastava, Vanya garg, Vivekta Singh..197 Improving the performance of Web log Mining by using K- Means clustering with Neural Network Vinita Srivastava..203 Higher Education Through Enterpreneurship Development in India Mrs. Vijay..208 Concepts ,Techniques,Limitations And Application of data mining S.C. Pandey, P.K Singh, D. dubey...210 ICT for Energy Efficiency, Conservation & reducing Carbon Emissions Aakash Mittal 212 Study of Ant Colony Optimization For Proficient Routing In Solid Waste Management Aashdeep Singh, Arun Kumar, Gurpreet Singh.213 Survey on Decision Tree Algorithm Jaya Bhushan, Shewta Rana, Indu...215

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Volume 2Analysis of Multidimensional Modeling Related To Conceptual Level Udayan Ghosh, Sushil Kumar....222 Wireless Sensor Networks Using Clustering Protocol Gurpreet Singh, Shivani Kang.....229 Performance Evaluation of Route optimization Schemes Using NS2 Simulation Manoj Mathur, Sunita Malik, Vikas...235 IT-Specific SCM Practices in Indian Industries: An Investigation Sanjay Jharkharia.....239 Cloud Computing Parveen Sharma, Manav Bharti University, Solan Himachal Pradesh..259 Comparing the effect of Hard and Soft threshold techniques on speech compression using wavelet transform Sucheta Dhir.263 A Hybrid Filter for Image Enhancement Vinod Kumar, Kaushal Kishore and Dr. Priyanka ...269 Comprehensive Study of Finger Print Detection Technique Vivekta Singh, Vanya Garg ....273 Study of Component Based Software Engineering using Machine Learning Techniques Vivekta Singh 281 Efficient Location-Based Spatial Query (LBSQ) Processing in Wireless Broadcast Environments K Madhavi, Dr Narasimham Challa.....285 A 3D Face Recognition using Histrograms Sarbjeet Singh, Meenakshi sharma, Dr. N Suresh Rao, Dr. Zahid Ali...291 An Application of Eigen Vector in Back Propagation Neural Network for Face expression Identification Ahsan Hussain ....295 Next Generation Cloud Computing Architecture Ahmad Talha Siddiqui, Shahla Tarannum, Tehseen Fatma.....299 Virtualization of Operating System using Xen Technology Annu Dhankhar, Siddharth Rana....304 Quality Metrics for TTCN-3 and Mobile-Web Applications Anu Saxena, Kapil Saxena..308 Future of ICT Enable Services for Inclusive Growth in Rural Unprivileged Masses Bikash Chandra Sahana , Lalu Ram.......312 Conversion of Sequential Code to Parallel An Overview of Various Conversion Methods Danish Ather, Prof. Raghuraj Singh .....314 Innovation And Entrepreneurship In Information And Communication Technology Deepak Sharma, Nirankar Sharma, Nimisha Shrivastava....320 Fuzzy Classification On Customer Relationship Management Mohd. Faisal Muqtida, Ashi Attrey, Diwakar Upadhyay...322 New Technology Acceptance Model to Predict Adoption of Wireless Technology in Healthcare Gaurav Singh , Manisha Yadav, Shivani Rastogi.330 Entrepreneurship through ICT for disadvantaged communities Ms. Geetu Sodhi, Mr. Vijay Gupta ....335 Efficient Location-Based Spatial Query (LBSQ) Processing in Wireless Broadcast Environments K Madhavi, Dr Narasimham Challa...342 K means Clustering Algorithm with High Performance using large data Vikas Chaudhary , Vikas Mishra, Kapil ...350 Performance Evaluation of Route optimization Schemes Using NS2 Simulation Manoj Mathur, Sunita Malik, Vikas...355 Image Tracking and Activity Recognition Navneet Sharma, Divya Dixit, Ankur Saxena...358

7An innovative digital watermarking process A Critical Analysis Sangeeta Shukla, Preeti Pandey, Jitendra Singh361 Survey On Decision Tree Algorithm Shweta Rana........368 Comparing the effect of Hard and Soft threshold techniques on speech compression using wavelet transform Sucheta Dhir.....374 Improving The Performance Of Web Log Mining By Using K-Mean Clustering With Neural Network Vinita Shrivastava...379 A Hybrid Filter for Image Enhancement Vinod Kumar, Kaushal Kishore, Dr. Priyanka ....385 Trends in ICT Track: Software development & Deployment (AGILE METHODOLOGIES) Shubh, Priyanka Gandh, Manju Arora....390 Vulnerabilities in WEP Security and Their Countermeasures Akhilesh Arora.....400 Implementation of Ethernet Protocol and DDS in Virtex-5 FPGA for Radar Applications Garima chaturvedi, Dr.Preeta sharan ,Peeyush sahay...408 CCK Coding Implementation in IEEE802.11b Standard Mohd. Imran Ali......413 Cognitive Radio and Management of Spectrum Prof.Rinkoo Bhatia, Narendra Singh Thakur, Prateek Bhadauria, Nishant Dev...416 Impact of MNCs on entrepreneurship Ms. Sonia......428 Multilayered Intelligent Approach - An Hybrid Intelligent Systems Neeta Verma, Swapna Singh....437 Green ICT: A Next Generation Entrepreneurial Revolution Pooja Tripathi441

ABSTRACTS AND PPTSAn Innovation Framework For Practice-Predominant Engineering Education Om Vikas ........450 Mobile Ad-hoc Network Apoorv Agarwal, Apeksha Aggarwal ....460 Fuzzy C- Mean Clustering Algorithm Arun Kumar Sharma........470 A Comparitive Study of Web Securioty Protocols Hanish Kumar......475 E-Village A new mantra for rural development Mr. S.K. Mourya......481 Green ICT: A Next Generation Entrepreneurial Revolution Prof Pooja Tripathi.....484 Role Of 21st Centaury : Ict Need Of The Day Saurabh Choudhry ........486 Reusability Of Software Components Using Clustering Meenakshi Sharma, Priyanka Kakkar, Dr. Parvinder Sandhu, Sonia Manhas.487

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PART - 1

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New Technology acceptance model to predict adoption of wireless technology in healthcare

Abstract Adoption of new technologies is researched in Information Systems (IS) literature for the past two decades, starting with the adoption of desktop computer technology to the adoption of electronic commerce technology. Issues that have been researched comprise of how users handle various options available in software environment, their perceived opinion, barriers and challenges to adopting a new technology, IS development procedures that are directly impacting any adoption including interface designs and elements of human issues. However, literature indicates that the models proposed in the IS literature such as Technology Acceptance Model (TAM) are not suitable to specific settings to predict adoption of technology. Studies in the past few years have strongly concluded that TAM is not suitable in healthcare setting because it doesnt consider a myriad of factors influencing adoption technology adoption in healthcare This paper discusses the problems in healthcare due to poor information systems development, factors that need to be considered while developing healthcare applications as these are complex and different from traditional MIS applications and derive a model that can be tested for adoption of new technology in healthcare settings. The contribution of this paper is in terms of building theory that is not available in the combined areas of Information Systems and healthcare. Index Terms healthcare, Information Systems, adoption factors.

I. INTRODUCTION

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nstitute of Medicine (IOM) in the United States has recognized that frontier technologies such as wireless technology would improve access to information in order to achieve quality health care. A

report released by the IOM in 2003 outlined a set of recommendations to improve Patient safety and reduce errors using reporting systems that are based on Information Systems (IS). While it is widely accepted that IS assists health related outcomes, how this can be efficiently achieved is an under researched area. Therefore, conflicting outcomes are reported in healthcare studies as to the successful role of IS. In essence, research is needed to investigate the role, and perhaps the use of, frontier technologies in improving information management, communication, cost and access to improve quality healthcare. In healthcare, specific issues relating to the failures of Information Management are being addressed using frontier technologies such as RF Tags and Wireless Handheld Devices. The main focus in using these technologies is to collect patient related information in an automated manner, at the point of entry, so as to reduce any manual procedures needed to capture data. While no other discipline relies more heavily on human interactions than health care, it is in healthcare that technology in the form of wireless devices has the means to increase not decrease the benefits derived from the important function of human interaction. Essential to this is the acceptance of this wireless handheld technology as this technology enables to collect data at the point of entry, with minimal manual intervention, with a higher degree of accuracy and precision. When it comes to the Management of Information Systems, development and implementation of a hospital Information System is different from traditional Information Systems due to the life critical environment in hospitals. Patient lives are dependent

10upon the information collected and managed in hospitals and hence smart use of information is crucial for many aspects of healthcare. Therefore, any investigation conducted should be multi-dimensional and should cover many aspects beyond technical feasibility and functionality dictated by traditional systems. Successful implementation of health information systems includes addressing clinical processes that are efficient, effective, manageable and well integrated with other systems). While traditional Information Systems address issues of integration with other systems, this is more so important in hospital systems because of the profound impact these systems have on short and long term care of patients .Reasons for failure in Information Systems developed for healthcare include lack of attention paid to the social and professional cultures of healthcare professionals, underestimation of complex clinical routines, dissonance between various stakeholders of health information, long implementation time cycles, reluctance to support projects financially once they are delivered and failures to learn from past mistakes. Therefore, any new technologies should address these reasons in order to be accepted in the healthcare setting. II. UNSUITABILITY OF CURRENT TECHNOLOGY ACCEPTANCE MODELS TO HEALTHCARE: The acceptance of new technologies has long been an area of inquiry in the MIS literature. The acceptance of personal computer applications, telemedicine, e-mail, workstations, and the WWW are some examples of technologies that have been investigated in the MIS literature. User technology acceptance is a critical success factor for IT adoption and many studies have predicted this using Technology Acceptance Model (TAM), to some extent, accurately by means of a host of factors categorized into characteristics of the individuals, characteristics of the technology and the characteristics of the organizational context. Technology Acceptance Model, specifically measures the determinants of computer usage in terms of perceived usefulness and perceived ease of use. While perceived usefulness has emerged as a consistently important attitude formation, studies have found that perceived ease of use has been inconsistent and of less significant. The literature suggests that a plausible explanation for this could be the continued prolonged users exposure to technology leading to their familiarity, and hence the ease in using the system. Therefore users could have interpreted the perceived ease of use as insignificant while determining their intention to use a technology. The strengths of TAM lies in the fact that it has been tested in IS with various sample sizes and characteristics. Results of these tests suggest that it is capable of providing adequate explanation as well predicting user acceptance of IT. Strong support can be found for the Technology Acceptance Model (TAM) to be robust in predicting user acceptance However, some studies criticize TAM for its examination of the model validity with students who have limited computing exposure, administrative and clerical staff, who do not use all IT functions found in software applications. Studies also indicate that the applicability of TAM to specific disciplines such as medicine is not yet fully established. Further, the validity and reliability of the TAM in certain professional context such as medicine and law is questioned. Only limited information is found in the healthcare related literature as to the suitability of TAM. Similarly, in the literature related to the legal field, especially where IT is referred, limited information can be found on TAM. Therefore, it appears that the model is not fully tested with various other professionals in their own professional contexts. Therefore, it can be argued that, when it comes to emerging technology such as wireless handheld devices, TAM may not be sufficient to predict the acceptance of technology because the context becomes quite different. It should be noted that the current context in healthcare related Information Systems is not only the physical environment but also the ICT environment as wireless technology is markedly different from Desktop technology. A major notable change is the way in which information is accessed using wireless technology as the information is pushed to the users as opposed to users pulling the information from desktop computers. In the Desktop technology, users have the freedom to choose what they want to access and the usage behavior is dependent upon their choice. On the other hand, using wireless devices, it is possible for the information whether needed or not to reach these devices assume significant importance because of the setting in which these devices are used. For example, in an operation theatre patient lives assume importance and information needs must reflect this. If wireless handheld devices dont support data management that are closely linked with clinical procedures due to device restrictions such as screen size and memory, despite their attractions, users would discard these devices. Therefore,

11applications developed onto these devices must address complex clinical procedures that can be supported by these devices. Another major consideration in the domain of wireless technology is the connectivity. While this is assumed to be always available in a wired network environment, this can not be guaranteed in a wireless technology due to mobility the network connectivity. As users carry the device and roam, the signal strength may change from strong to weak and this may interrupt user operations. Therefore, to accomplish smart information management, certain technical aspects must also be addressed. Current users of wireless technology are concerned with their security and privacy aspects associated in using this technology. This is because they need to reveal their identity in order to receive information. While the privacy is concerned with the information that they provide to others, security threats fall under the categories of physical threat and data threat. Due to the infancy stages and hardware restrictions, handheld devices are not able to implement these features to the expected level on the devices as found in desktop computers. In a healthcare setting, any leak in the privacy issues would have potential adverse impact on the stakeholders. Further, due to other devices that may be using radio frequency or infra-red frequency in providing healthcare to patients, there may be practical implementation restrictions in the usage of wireless devices for ICT. Our own experience in providing wireless technology solutions to a private healthcare in Western Australia yielded mixed responses. The wireless technology developed and implemented for the Emergency Department was successful in terms of software development and deployment. The project was well accepted by the users in the healthcare. However, the wireless solution provided to address problems encountered in the Operation Theatre Management System was not well received by the users, despite the superiority in design, functionality and connectivity. Users were reluctant to use the application due to the hardware and database connectivity restrictions, despite scoring a high level of opinion on acceptance for usefulness and ease of use. Now, let us assume that TAM is correct in claiming that the intention to use a particular system is a very important factor in determining whether users will actually use it. Let us also assume that the wireless systems developed for the private healthcare provider in Western Australia exhibited that there were clear intentions to use a the system. However, despite a positive affect on perceived usefulness and perceived ease of use, the wireless system was not accepted by users. It should be noted that the new system mimicked the current traditional system, and yet did not yield any interest in terms of user behaviors. While searching for reasons for this hard to explain phenomena, who argued, after studying TAM, that perceived usefulness should also include near-term and long-term usefulness in order to study behavioral intentions. Other studies that have examined the utilization of the Internet Technology have also supported view. This has given us a feeling that TAM may not be sufficient to predict the acceptance of wireless technology in specific healthcare setting. A brief review of prior studies in healthcare indicated that a number of issues associated with the lack of acceptance of wireless handheld devices are highlighted but not researched to the full extent that they warrant. For example, drawbacks of these devices in healthcare included perceived fear for new learning by doctors, time investment needed for such learning, cost involved in setting up the wireless networks and the cost implications associated with the integration of existing systems with the new wireless system .A vast majority of these studies concur that wireless handheld devices would be able to provide solutions to the Information Management problems encountered by healthcare. While these studies unanimously agree that the information management would be smarter using wireless technology and handheld devices, they seldom provided details of those factors that enabled the acceptance of wireless technology specific to healthcare setting. MIS journals appear to be lagging behind in this area. Therefore, it is safe to assume that current models that predict the acceptance of technology based on behavioral intentions are insufficient. This necessitates a radically new model in order to predict the acceptance of wireless handheld technology in specific professional settings.

III. INGREDIENTS FOR A NEW MODEL TO PREDICT ACCEPTANCE OF NEW TECHNOLOGY: Some of the previous models measured actual use through the intention to use and input to these models are perceived usefulness, perceived ease of use, attitude, subjective norm, perceived behavioral control, near term use, short term use, experience, facilitating conditions and so on. In recent years, factors that impacting technology acceptance

12included job relevance, output quality and result demonstrability. In the field of electronic commerce and mobile commerce, factors such as security and trust are considered as factors of adoption of these technologies. In end user computing, factors such as user friendliness and maintainability appear to be influencing the applications. Therefore, any new model to determine the acceptance of wireless technology would include some of the above factors. In addition to these, when it comes to wireless technology, any acceptance factors should hinge on two dominant concepts hardware (or device) and applications that run o the hardware as the battle continues to accommodate more applications on a device that is diminishing in size, but improving in power. Further, mobile telephones and PDAs, appear to be accepted based on their attractiveness, hardware design, type of key pad that they provide, screen color and resolution, ability to be carried around etc. In effect, the hardware component appears to be an equally dominant factor in the adoption of wireless technology. Once the hardware and software applications are accepted, the third dominant factor in the acceptance of wireless technology appears to be the telecommunication factor. This factor involves various services provided by telecommunication companies, the cost involved in such services, the type of connectivity, roaming facilities, ability to access the Internet, provision for Short Messaging Services (SMS), ability to play games using the mobile devices etc. These factors are common to both mobile telephones and emerging PDAs. Some common features that the user would like to see appear to be alarming services, calendar, scheduler, ability to access digital messages both text and voice etc. Therefore, studies that investigate the adoption of wireless technology should aim to categories factors based on hardware, applications and telecommunication as these appear to be the building blocks of any adoption of this technology. Specific factors for applications, perhaps, could involve portability across various hardware, reliability of code, performance, ease of use, module cohesion across different common applications, clarity of code etc,. In terms of hardware, the size of the device, memory size, key pad, resolution of screen, various voice tones, portability, attractiveness, brand names such as Nokia, capability such as alarms, etc. would be some of the factors of adoption or acceptance. In terms of service provision, plan types, costs, access, free time zones, SMS provision, cost for local calls, cost to access the Internet, provision to share information stored between devices etc. appear to be dominant factors. Factors such as security etc form a common theme as all the three dominant categories need to ensure this factor. Factors mentioned above are crucial to determine the development aspects of Wireless Information Systems (WIS) for healthcare as these factors dictate the development methodology, choice of software language, user interface design etc. Further, the factors of adoption in conjunction with methodology would determine the integration aspects such as coupling the new system with existing systems. This would then determine the implementation plans. In essence, an initial model that can determine the acceptance of wireless technology in healthcare can be portrayed as follows:

Diagram 1: Proposed Model for Technology Adoption in Healthcare Settings In the above model, the three boxes in dark borders show the relationship between various factors that influence the acceptance of technology. The box on the left indicates various factors influencing wireless technology I any given setting. The three categories of factors hardware, software and telecommunication affect the way in which wireless technology is implemented. The factors portrayed in the box are generic and their role to specific healthcare setting varies depending upon the level of implementation. Once the technology is implemented, it is expected to be used. In healthcare settings, it appears that the usage, relevance and need are the three most important influencing factors for the continual usage of new technology When the correct balance is established, users exhibit positive perceptions about using a new technology such as wireless handheld devices for data management purposes. This, in turn, brings out positive attitude towards using the system, both short

13and long term usage. The positive usage would then determine the intentions to use, resulting in usage behavior. The usage behavior then determines the factors that influence the adoption of new technology in a given setting. This is shown by the arrow that flows from right to left. Based on the propositions made in the earlier paragraphs, it is suggested that any testing done to predict the acceptance of new technology in healthcare should test the following hypotheses: 1. Hardware factors have a direct effect on the development, integration and implementation of wireless technology in healthcare for data management 2. Software factors have a direct effect on the development, integration and implementation of wireless technology in healthcare for data management 3. Telecommunication factors direct effect on the development, integration and implementation of wireless technology in healthcare for data management 4. Factors influencing wireless technology in healthcare setting have direct positive effect on usage, relevance and need 5. User perception of new technology is directly affected by usage, relevance and need 6. User perception of new technology has a direct effect on user attitude in using such technology 7. User attitude has a direct effect on intentions to use a new technology 8. Usage behavior is determined by intentions to use a new technology of those key factors. This approach would complement the open ended questions so as to determine the importance of the individual factors determining the adoption and usage of wireless devices and applications.

V. DATA COLLECTION: In order to perform validity and reliability tests, a minimum of 250 samples are required. Any study to test the model should consider the randomness of the samples to avoid any collective bias. Similarly, about 50 samples may be required to undergo the interview process, with each interview to last for 60 minutes. Any instruments developed for testing the model should be able to elicit responses of 'how' and 'why'. This is essential in order to discern differences between adoption and usage decision of wireless handheld applications. In addition, comparing responses to the question about adoption and questions about use would provide evidence that respondents were reporting their adoption drivers and not simply their current behavior. The interview questions should be semi structured or partially structured to guide the research. There are variations in qualitative interviewing techniques such as informal, standardized and guided. Structured interviews and partially structured interviews can be subjected to validity checks similar to those done in quantitative studies. Samples could be asked about their usage of wireless devices including mobile telephones and other hospital systems during the initial stages of the interview. They could be interviewed further so as to identify factors that would lead to the continual usage of these devices and any emerging challenges that they foresee such as training. The interviews can be recorded on a digital recording system with provision to convert automatically to a PC to avoid any transcription errors. This approach would also minimize transcription time and cost. The interview questions should be developed in such as way that both determinants and challenge factors could be identified. This then increases or enhances the research results, which is free of errors or bias.

IV. INSTRUMENTS: The instruments typically would constitute two broad categories of questions. The first category of questions would be related to the adoption and usage of wireless applications in healthcare for data collection purposes. The second category would consist of demographic variables, as these variables determine the granularity of the setting. Open ended questions can be included in the instrument to obtain unbiased and non-leading information. Prior to administering the questions, a complete peer review and a pilot study are insisted in order to ascertain the validity of the instrument. A two stage approach can be used in administering the instrument, where the first stage would gather information about the key factors influencing users decisions to use wireless applications and the second stage on the importance

VI. DATA ANALYSIS: Data should be coded by two individuals into a computer file prior to analysis and a file comparator technique should be used to resolve any data entry errors. A coding scheme should also be developed based on the instrument developed. The coders

14should be given sufficient instructions on the codes, anticipated responses and any other detail needed to conduct the data entry. Coders should also be given a start-list that will include definitions from prior research for the categories of the construct. Some of the categories would include utilitarian outcomes such as applications for personal use and barriers such as cost and knowledge. Data should be analyzed using statistical software applications using both quantitative and qualitative analyses. Initially a descriptive analysis needs to be conducted, including a frequency breakdown. This should then be followed by a detailed cross sectional analysis of the determinants of behavior. A factor analysis should also be conducted to identify factors of adoption. Once this is completed, tests for significance can be performed between various factors.[4] Freeman, E. H. (2003). Privacy Notices under the GrammLeach-Bliley Act. Legally Speaking (May/June), 5-9. [5] Goh, E. (2001). Wireless Services: China (Operational Management Report No. DPRO-94111): Gartner. [6] Hu, P. J., Chau, P. Y. K., & Liu Sheng, O. R. (2002). Adoption of telemedicine technology by health care organizations: An exploratory study. Journal of organizational computing and electronic commerce, 12(3), 197-222. [7] Hu, P. J., Chau, P. Y. K., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91-112. [8] Kwon, T. J., & Zmud, R. W. (Eds.). (1987). Unifying the fragmented models of information systems implementation. New York: John Wiley. [9] Oritz, E., & Clancy, C. M. (2003). Use of information technology to improve the Quality of Health Care in the United States. Health Services Research, 38(2), 11-22. [10] Remenyi, D., Williams, B., Money, A., & Swartz, E. (1998). Doing Research in Business and Management. London: SAGE Publications Ltd. [11] [12] Rogers, E. M. (1995). Diffusion of Innovation (4th ed.). New York: Free Press. [13] Rozwell, C., Harris, K., & Caldwell, F. (2002). Survey of Innovative Management Technology (Research Notes No. M-15-1388): Gartner Research. [14] The nature and determinants of IT acceptance, routinization, and infusion, 67-86 (1994). [15] Sausser, G. D. (2003). Thin is in: web-based systems enhance security, clinical quality. Healthcare Financial Management, 57(7), 86-88. [16] Simpson, R. L. (2003). The patient's point of view -- IT matters. Nursing Administration Quarterly, 27(3), 254-256. [17] Smith, D., & Andrews, W. (2001). Exploring Instant Messaging: Gartner Research and Advisory Services. [18] Sparks, K., Faragher, B., & Cooper, C. L. (2001). Well-Being and Occupational Health in the 21st Century Workplace. Journal of Occupational and Organizational Psychology, 74(4), 481-510. [19] Tyndale, P. (2002). Taxonomy of Knowledge Management Software Tools: Origins and Applications, 2002, from www.sciencedirect.com [20] Wiebusch, B. (2002). First response gets reengineered: Will a new sensor and the power of wireless communication make us better prepared to deal with biological attacks? Design News, 57(11), 63 - 68. [21] Wisnicki, H. J. (2002). Wireless networking transforms healthcare: physician's practices better able to handle workflow, increase productivity (The human connection). Ophthalmology Times, 27(21), 38 - 41. [22] Yampel, T., & Eskenazi, S. (2001). New GUI tools reduce time to migrate healthcare applications to wireless. Healthcare Review, 14(3), 15-16. 9

VII.

CONCLUSION:

We saw in this case study that there is a necessity for a new model to accurately predict the adoption of new technologies in specific healthcare setting because current models available in the Information Systems domain are yet to fulfill this need. Based on our experience and available literature, we identified some initial factors that can influence and determine acceptance of technology. We also proposed a theoretical model that can be tested using these initial factors. In order to be complete, we suggested a proposed methodology for testing the model.

VIII. REFERENCES:[1] Davies, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Communications of the ACM, 35(8), 9821003. Davis, G. B. (1985). A typology of management information systems users and its implication for user information satisfaction research. Paper presented at the 21st Computer Personnel Research Conference, Minneapolis. Dyer, O. (2003). Patients will be reminded of appointments by text messages. British Medical Journal, 326(402), 281.

[2]

[3]

15

Solutions to Security and Privacy Issues in Mobile Social Networking

Abstract Social network information is now being used in ways for which it may have not been originally intended. In particular, increased use of smartphones capable of running applications which access social network information enable applications to be aware of a users location and preferences. However, current models for exchange of this information require users to compromise their privacy and security. We present several of these privacy and security issues, along with our design and implementation of solutions for these issues. Our work allows location-based services to query local mobile devices for users social network information, without disclosing user identity or compromising users privacy and security. We contend that it is important that such solutions be accepted as mobile social networks continue to grow exponentially.

IX. INTRODUCTION

O

ur focus is on security and privacy in locationaware mobile social network (LAMSN) systems. Online social networks are now used by hundreds of millions of people and have become a major platform for communication and interaction between users. This has brought a wealth of information to application developers who develop on top of these networks. Social relation and preference information allows for a unique breed of application that did not previously exist. Furthermore, social network information is now being correlated with users physical locations, allowing information about users preferences and social relationships to interact in real-time with their physical environment. This fusion of online social networks with real-world mobile computing has created a fast growing set of applications that have unique requirements and unique implications that are not yet fully understood. LAMSN systems such as WhozThat [1] and Serendipity [2] provide the

infrastructure to leverage social networking context within a local physical proximity using mobile smartphones. However, such systems pay little heed to the security and privacy concerns associated with revealing ones personal social networking preferences and friendship information to the ubiquitous computing environment. We present significant security and privacy problems that are present in most existing mobile social network systems. Because these systems have not been designed with security and privacy in mind, these issues are unsurprising. Our assertion is that these security and privacy issues lead to unacceptable risks for users of mobile social network systems. We make three main contributions in this paper. a) We identify three classes of privacy and security problems associated with mobile social network systems: (1)direct anonymity issues, (2) indirect or K-anonymity issues, and (3) eavesdropping, spoofing, replay, and wormhole attacks. While these problems have been examined before in other contexts, we discuss how these problems present unique challenges in the context of mobile social network systems. We motivate the need for solutions to these problems. b) We present a design for a system, called the identity server, that provides solutions for these security and privacy problems. The identity server adapts established privacy and security technologies to provide novel solutions to these problems within the context of mobile social network systems. We describe our implementation of the identity server.

X. BACKGROUND In this section we provide the reader with a short introduction to work in the area of mobile social

16networking and the technologies that have made it possible. 2.1 MOBILE COMPUTING Smartphones now allow millions of people to be connected to the Internet all the time and support mature development environments for third-party application developers. Recently there has been a dramatic rise in usage of smartphones, those phones capable of Internet access, wireless communication, and supporting development of third-party applications. This rise has been due largely to the iPhone and iPod Touch. 2.2 SOCIAL NETWORKS The growth of social networks has exploded over the last year. In particular, usage of Facebook has spread internationally and to users of a wide age range. According to Facebook.coms statistics page, the site has over 200 million active users [4] [5], of which ove 100 million log on To compare this with Com Scores global Internet usage statistics [6], this would imply that nearly 1 in 10 of all Internet users log on to Facebook everyday and that he active Facebook Internet population is larger than any single countrys Internet population (China is the largest with 179.7 million Internet users [6]). 2.3 PRIVACY AND SECURITY The work described in this paper draws on some previous privacy research in both location-based services and social networks [12] [13]. This prior work does not approach the same problem as addressed in this paper, however the mechanisms used in these papers may provide certain functions necessary to associate user preferences anonymously with user location for use in third-party applications. Our work, however, differs in that it seeks to hide the users identity while distributing certain personal information obtained from existing online social networks. XI. SECURITY AND PRIVACY PROBLEMS Peer-to-peer mobile social network systems, like WhozThat and Social Aware, exchange users social network identifiers between devices using shortrange wireless technology such as Bluetooth. In contrast to these systems, a mobile device in clientserver mobile social network systems, such as Bright kite and Loop, notifies a centralized server about the current location of the device (available via GPS, cell-tower identification, or other mechanisms). By querying the server, mobile devices in these clientserver systems can find nearby users, information about these nearby users, and other items of interest. 3.1 Direct Anonymity Issues The information exchange model of the mobile social network systems discussed previously provide little protection for the users privacy. These systems require the user to allow access to his or her social network profile information and at the same time associate that information with the users identity. For instance, Facebook applications generally require the user to agree to give the application access to his/her information through Face books API, intrinsically tying such information to the users identity. In a peer-to-peer context-aware mobile social network system such as Social Aware, we can track a user by logging the date and time that each mobile or stationary device detects the users social network ID. By collecting such logs, we can construct a history of the locations that a user has visited and the times of each visit, compromising the users privacy. Finally, given access to a users social network ID, someone else could access that users public information in a way that the user may not have intended by simply viewing that users public profile on a social network Web site. We conclude that clear text exchange of social networking IDs in systems such as WhozThat and Social Aware leads to unacceptable security and privacy risks, and allows the users anonymity to be easily compromised. We call such problems that directly compromise a users anonymity direct anonymity attacks. Direct anonymity attacks are also possible in client-server mobile social network systems. While users social network IDs are generally not directly exchanged between mobile devices in such systems, mobile or stationary devices can still track a user by logging the date and time that each device finds the user nearby. Since each device in these systems can find the social network user names and often full names of nearby users, the privacy of these users can be compromised. Thus, we have a direct anonymity issue - exposure of user names and locations in client-server systems allows the users anonymity to be compromised. 3.2. The Indirect or K-Anonymity Problem the indirect anonymity problem exists when a piece of information indirectly compromises a users identity. An example of this is when a piece of information unique to a user is given out, such as a list of the users favorite movies, this information might then be easily mapped back to the user. The Kanonymity problem occurs when n pieces of information or n sets of related information can be

17used together to uniquely map back to a users identity. Furthermore, if a set of information can only be mapped to a set of k or fewer sets of users, the users anonymity is still compromised to a degree related to k. The challenge is to design an algorithm that can decide what information should and should not be given out in order to guarantee the anonymity of associated users. This problem is similar to previous K-anonymity problems related to the release of voter or hospital information to the public. However, it has been shown that by correlating a few data sets a high percentage of records can be reidentified. A paper by Sweeney shows how this reidentification process is done using voter records and hospital records [17]. The K-anonymity problem in this paper is unique in that the standard K-anonymity guarantees that released information cannot distinguish between k 1 individuals associated with the released information. However, the problem discussed here does not involve the release of personal records but rather sets of aggregated information that may relate to sets of individuals that may or may not be associated with the released information. Therefore, the K-anonymity guarantee for our problem refers to the minimal number of indistinguishable unique sets that are sufficient to account for all released information. More precisely there must be no more than k unique sets that are 1 not subsets of each other and all other sufficient sets are supersets of some of the minimal sets. This paper presents this K-anonymity problem informally and proposes a solution that is currently being explored and implemented by the authors, however it does not formally solve this problem, which is proposed as an important open problem in the area of mobile social network privacy. We argue that this problem is important because it would provide an alternative for users to take advantage of new mobile social network applications without compromising their privacy. The K-anonymity problem applies to both peer-to-peer and client server mobile social network systems, since both systems involve sharing a users social network profile data with other users of these systems 3.3 Eavesdropping, Spoofing, Replay, and Wormhole Attacks Once a users social network ID has been intercepted in a peer-to-peer mobile social network system, it can be used to mount a replay and spoofing attack. In a spoofing attack, a malicious user can masquerade as the user whose ID was intercepted (the compromised user) by simply sending (replaying) the intercepted ID to mobile or stationary devices that request the users social network ID. Thus, the replay attack, where the compromised users ID is maliciously repeated, is used to perform the spoofing attack. Another specific type of replay attack is known as a wormhole attack [18], where wireless transmissions are captured on one end of the network and replayed on another end of the network. these attacks could be used for a variety of nefarious purposes. For example, a malicious user could masquerade as the compromised user at a specific time and place while committing a crime. Clearly, spoofing attacks in mobile social networking systems present serious security risks. In addition to intercepting a users social network ID via eavesdropping of the wireless network, a malicious user could eavesdrop on information transmitted when a device requests a users social network profile information from a social network server. For example, if a mobile device in a peer-topeer system uses HTTP (RFC 2616) to connect to the Facebook API REST server [19] instead of HTTPS (RFC 2818), all user profile information requested from the Facebook API server is transmitted in clear text and can be intercepted. Interception of such data allows a malicious user to circumvent Face books privacy controls, and access private user profile information that the user had no intention to share. Eavesdropping, spoofing, replay, and wormhole attacks are generally not major threats to client-server mobile social network systems. These attacks can be defended against with the appropriate use of a robust security protocol such as HTTPS, in conjunction with client authentication using user names and passwords or client certificates. If a users social network login credentials (user name and password, or certificate) have not been stolen by a malicious user and the user has chosen an appropriately strong password, then it is nearly impossible for the malicious user to masquerade as that user. .

XII. SECURITY AND PRIVACY SOLUTIONS We have designed and implemented a system, called the identity server, to address the security and privacy problems described previously. Our system assumes that each participating mobile device has reasonably reliable Internet access through a wireless wide area network (WWAN) cell data connection or through a WiFi connection. Mobile devices that lack such an Internet connection will not be able to participate in our system. Furthermore, we assume that each participating mobile device has a short-range wireless network interface, such as either Bluetooth or WiFi,

18for ad-hoc communication with nearby mobile and/or stationary devices. We describe the design and implementation of the identity server in this section 4.1Design of the Identity Server and Anonymous Identifier As discussed in subsections III-A and III-C, the clear text exchange of a users social network ID presents significant privacy and security risks [20]. To address these risks, we propose the use of an anonymous identifier, or AID. The AID is a nonce that is generated by a trusted server, called the identity server (IS). Before a users mobile device advertises the users presence to other nearby mobile and stationary devices, it securely contacts the IS to obtain the AID. The IS associates the newly generated AID with the mobile device that requested the AID, and then returns the new AID to the mobile device. The users mobile device then proceeds to share this AID with a nearby mobile and/or stationary device by launching a Bluetooth AID sharing service. After a nearby mobile or stationary device (device B) discovers this AID sharing service on the users mobile device (device A), device B establishes a connection to the users mobile device to obtain the shared AID. After the AID has been obtained by device B, device A requests another AID from the IS. This new AID will be shared with the next mobile or stationary device that connects to the AID sharing service on device A. While our design and implementation uses Bluetooth for AID sharing, we could also implement AID sharing using WiFi After the device B obtains the shared AID from device A, device B then proceeds to query the IS for the social network profile information for the user that is associated with this AID. Figure 1 shows the role of the IS in generating AIDs and processing requests for a users social network information. Once the social network information for an AID has been retrieved by the IS, the IS removes this AID from the list of AIDs associated with the mobile user. Before the users mobile device next advertises the users presence using the Bluetooth AID sharing service, it will obtain a new AID from the IS as described above. We permit multiple AIDs to be associated with a mobile user, which allows for multiple nearby mobile or stationary devices to obtain information about the user. To improve efficiency, the users mobile device may submit one request for multiple AIDs to the IS, and then proceed to share each AID one at a time with other nearby devices. The IS sets

a timeout value for each AID when the AID is created and provided to a users mobile device. An AID times out if it is not consumed within the timeout period, that is, if the IS has not received a query for social network profile information for the user associated with this AID within the timeout period. Upon timeout of an AID, the IS removes the AID from the list of AIDs associated with the user. We use AID timeouts to prevent the list of AIDs associated a user from growing without bound. The use of AIDs in our system provides important privacy features for mobile users. Since the mobile device shares only AIDs with other devices, a malicious user who has intercepted these AIDs cannot connect these AIDs to a particular users social network identity. Furthermore, the IS does not support the retrieval of certain personally identifiable information from a users social network profile, such as the users full name, email address, phone number, etc. Since the IS does not support the retrieval of personally identifiable information, a device that retrieves social network information for the user associated with an AID is unable to connect the AID to the users social network identity. Thus, only by compromising the IS can a malicious user tie an AID to a users social network ID. We assume that the IS is a secure and trusted system, and that compromising such a system would prove to be a formidable task. The use of IS and AIDs as we have described solves the direct anonymity problem. As the reader will see in subsection IV-C, the IS also addresses the indirect anonymity problem by providing a K-anonymity

19guarantee for information returned from users social network profiles. 4.2. Implementation of the Identity Server All IS services accessed by mobile and/or stationary devices are exposed as web services conforming to the 4.3 Trust Networks and Onion Routing One way to support privacy in social network applications is to transfer information using a trusted peer-to-peer network [29]. Such a network would require a trust network much like that used by Katz and Gold beck [30] in which social networks provided trust for default actions on the. Moreover, in a mobile social network application, nodes could not only share their information directly but could give permission to their trusted network to share their information. This approach was used in the One Swarm [31] system to allow peer-to-peer file sharing with privacy settings that allowed the user to share data publicly, just with friends, or even with a chosen subset of those friends. However, such a model has obvious problems if any nodes are compromised since information is easily associated with its source. These peer-to-peer networks could be made anonymous through the use of onion routing [32]. The Tor network [33] uses onion routing to allow nodes to send data anonymously. Through the use of layers of encryption that are decrypted at selected routers along a virtual route, routing nodes cannot directly relate the information at the destination to its source. If data was shared in this manner it would not be so easy to identify the source of the information, protecting the direct anonymity of the user. We are currently exploring the use of trust networks and onion routing in terms of taking a more decentralized approach to protecting user anonymity that does not require trust of the social network (such as Facebook itself [29].

REST architecture [21]. We used the open source Reset framework [22] for Java to develop the IS. We expose each resource on the IS, including a mobile users AID, a mobile users current location, and the Facebook profile information for a mobile user, as separate URL-accessible resources supporting HTTP GET, POST, and PUT methods as appropriate. Figure 2 shows the web-accessible resources exposed on the IS, along with the HTTP methods supported by each resource. The body of each HTTP request is encoded using JSON (RFC 4627). All web service network traffic between the IS and other mobile/stationary devices is encrypted using HTTPS, and access to all resources is authenticated using HTTP basic access authentication (RFC 2617). Each mobile user must sign up for a user account on the IS prior to participation in our system. During the signup process, the user provides his/her Facebook user ID (we can obtain this using Facebook Connect [23]), and chooses a user name and password. The users user name and password are securely stored on the users mobile device, and are used to authenticate with the IS and obtain access to the guarded web resources on the IS for the devices current location, the users AID, and the users Facebook profile information. Access to the web resources for the users AID and current location is available only to the user herself/himself, and no other entity save for the logic implemented on the IS. Access to the web resource for the users Facebook profile information (we call this user user A) is provided to any authenticated user with a user account on the IS, provided that the authenticated users device is within an acceptable range of user As mobile device. See below for more information on location-based access control for a users Facebook profile.

20CONCLUSION We have identified several important privacy and security issues associated with LAMSN systems, along with our work on novel solutions for these issues. Our solutions support anonymous exchange of social network information with real world locationbased systems, enabling context-aware systems that do not compromise users security and privacy. We hope that our work will convince users and developers that it is possible to move forward with creative mobile social network applications without further compromising user security and privacy. REFERENCES [1] N. Eagle and A. Pentland, Social serendipity: Mobilizing social software, [2] Global internet use reaches 1 billion, http://www.comscore.com/press/ release.asp?press=2698. [3] C. M. Gartrell, Social aware: Context-aware multimedia presentation via mobile social networks, Masters thesis, University of Colorado at Boulder, December 2008 applic [4] E. Miluzzo, N. D. Lane, S. B. Eisenman, and A. T. Campbell, Cenceme - injecting sensing presence into social networking applications, in Proceedings of the 2nd European Conference on Smart Sensing and Context (EuroSSC 2007), October 2007. [5] Brightkite, http://brightkite.com. [6] Loopt, http://www.loopt.com. [7] A. Tootoonchian, K. K. Gollu, S. Saroiu, Y. Ganjali, and A. Wolman, Lockr: social access control for web 2.0, in WOSP 08: Proceedings of the first

21

Wireless Monitoring Of The Green House Using ATMEGA Based Monitoring System: WSN Approach

Abstract in the present paper, authors have given an emphasis on WSN approach for green house monitoring and control. A control system is developed and tested using recent atmega microcontroller. The farmers in the developing countries can easily use designed for maximising yield. Atmega microcontrollers are preferred over other microcontrollers due to some important features including 10- bit ADC, sleep mode , wide input voltage range and higher memory capacity. Index Terms WSN, AVR, MICROCONTROLLERS, GREEN PRECISION AGRICULTURE RF2.4, HOUSE,

of the automation system architecture in modern greenhouses. Wireless communication can be used to collect the measurements and to communicate between the centralized control and the actuators located to the different parts of the greenhouse. In advanced WSN solutions, some parts of the control system itself can also be implemented in a distributed manner to the network such that local control loops can be formed. Compared to the cabled systems, the installation of WSN is fast, cheap and easy. Moreover, it is easy to relocate the measurement points when needed by just moving sensor nodes from one location to another within a communication range of the coordinator device. If the greenhouse flora is high and dense, the small and light weight nodes can even be hanged up to the plants branches. WSN maintenance is also relatively cheap and easy. The only additional costs occur when the sensor nodes run out of batteries and the batteries need to be charged or replaced, but the lifespan of the battery can be several years if an efficient power saving algorithm is applied. The research on the use of WSN in agriculture is mainly focused primarily on areas such as Proof-ofconcept applications to demonstrate the efficiency and efficacy of using sensor networks to monitor and control agriculture management strategies. The attempt is made by the authors to show the effective utilization of this concept into day to day monitoring of the green house for higher yield. II. RF COMMUNICATION AND MONITORING OF THE GREEN HOUSE PARAMETERS RF is the wireless transmission of data by digital radio signals at a particular frequency. RF

I.

INTRODUCTION

A

recent survey of the advances in wireless sensor network applications has reviewed a wide range of applications for these networks and identified agriculture as a potential area of deployment together with a review of the factors influencing the design of sensor networks for this application. WSN is a collection of sensor and actuators nodes linked by a wireless medium to perform distributed sensing and acting tasks. The sensor nodes collect data and communicate over a network environment to a computer system, which is called, a base station. Based on the information collected, the base station takes decisions and then the actuator nodes perform appropriate actions upon the environment. This process allows users to sense and control the environment from anywhere. There are many situations in which the application of the WSN is preferred, for instance, environment monitoring, product quality monitoring, and others where supervision of big areas is necessary. Wireless sensor network (WSN) form a useful part

22communication works by creating electromagnetic waves at a source and being able to send the electromagnetic waves at a particular destination. These electromagnetic waves travel through the air at near the speed of light. The advantages of a RF communication are its wireless feature so that the user neednt have to lay cable all over the green house. Cable is expensive, less flexible than RF coverage and is prone to damage. RF communication provides extensive hardware support for packet handling, data buffering, burst transmissions, clear channel assessment and link quality. A. FEATURES a) Low power consumption. b) High sensitivity (type -104dBm) c) Programmable output power -20dBm~1dBm d) Operation temperature range -40~+85 deg C e) Operation voltage: 1.8~3.6 Volts. f) Available frequency at : 2.4~2.483 GHz B. APPLICATIONS a) Wireless alarm and security systems b) AMR-automatic Meter Reading c) Wireless Game Controllers. d) Wireless Audio/Keyboard/Mouse C. PROPOSED RF COMMUNICATION BASED GREEN HOUSE PARAMETER MONITORING HARDWARE In the proposed hardware, there would be two section master and slave. The slave part would contain the temperature and humidity sensor. The sensor would be connected to the AVR microcontroller. The RF transceiver would be connected to the AVR microcontroller which would wirelessly send the data to the master part. The master part would contain the RF transceiver which would receive the data and give to the microcontroller. The count would be displayed on the graphics LCD. The motor and DC fan would also be connected to the master board. These motor and DC fan would be accordingly controller based upon the relevant temperature and humidity condition. The major components of the proposed hardware,as seen in fig.1, are, Microcontroller - AVR- Atmega 16, Atmega 32. Compiler : AVR studio Range - 150 meter Master and Slave communication: 247 slaves. Sensor: Temperature : LM35 and Humidity sensor

III.

WHY TO USE ATMEGA MICROCONTROLLER?

There are several features of Atmega microcontroller as given below which makes it an ideal choice for green house parameter monitoring. A. FEATURES a) High-performance, Low-power AVR 8-bit Microcontroller b) Advanced RISC Architecture c) High Endurance Non-volatile Memory segments 16K Bytes of In-System Self-programmable Flash program memory 512 Bytes EEPROM 1K Byte Internal SRAM d) Peripheral Features Two 8-bit Timer/Counters with Separate Prescalers and Compare Modes One 16-bit Timer/Counter 8-channel, 10-bit ADC e) Special Microcontroller Features Power-on Reset and Programmable Brown-out Detection Internal Calibrated RC Oscillator External and Internal Interrupt Sources B.ARCHITECTURAL DESCRIPTION The ATmega16 provides the following features: 16K bytes of In-System Programmable flash Program memory with Read-While-Write capabilities, 512 bytes EEPROM, 1K byte SRAM, 32 general purpose I/O lines, 32 general purpose working registers, a JTAG interface for Boundary scan, On-chip Debugging support and programming, three flexible

23Timer/Counters with compare modes, Internal and External Interrupts, a serial programmable USART, a byte oriented Two-wire Serial Interface, an 8channel, 10-bit ADC with optional differential input stage with programmable gain (TQFP package only), a programmable Watchdog Timer with Internal Oscillator, an SPI serial port, and six software selectable power saving modes. The Idle mode stops the CPU while allowing the USART, Two-wire interface, A/D Converter, SRAM; Timer/Counters, SPI port, and interrupt system to continue functioning. The Power-down mode saves the register contents but freezes the Oscillator, disabling all other chip functions until the next External Interrupt or Hardware Reset. In Power-save mode, the Asynchronous Timer continues to run, allowing the user to maintain a timer base while the rest of the device is sleeping. The ADC Noise Reduction mode stops the CPU and all I/O modules except Asynchronous Timer and ADC, to minimize switching noise during ADC conversions. In Standby mode, the crystal/resonator Oscillator is running while the rest of the device is sleeping. This allows very fast start-up combined with low-power consumption. In Extended Standby mode, both the main Oscillator and the Asynchronous Timer continue to run.The device is manufactured using Atmels high density nonvolatile memory technology. The Onchip ISP Flash allows the program memory to be reprogrammed in-system through an SPI serial interface, by a conventional nonvolatile memory programmer, or by an On-chip Boot program running on the AVR core. The boot program can use any interface to download the application program in the Application Flash memory. Software in the Boot Flash section will continue to run while the Application Flash section is updated, providing true Read-While-Write operation. By combining an 8-bit RISC CPU with In-System Self-Programmable Flash on a monolithic chip, the Atmel ATmega16 is a powerful microcontroller that provides a highly-flexible and cost-effective solution to many embedded control applications. IV. WHY RF 2.4?

(Abbreviations: SOC: System-on-Chip, Network Processor, TXRX: Transceiver)

NP:

Table II. Part number, minimum and maximum frequency range, operating voltage and description

In nutshell, the advantages of RF 2.4 are, a) Low power consumption. b) Integrated data filters. c) High sensitivity d) Operation temperature range -40~+85 deg C e) Available frequency at : 2.4~2.483 GHz- No certification f) Required from government V. DETILS OF THE SENSORS USED

The important features given below in table I and table II make RF 2.4 an ideal choice for green house parameter Monitoring Table I. Part number, status, device type, frequency range and sensitivity

A. TEMPERATURE SENSOR The LM35 series, shown in fig.3, are precision integrated-circuit temperature sensors, whose output voltage is linearly proportional to the Celsius (Centigrade) temperature. The LM35 thus has an advantage over linear temperature sensors calibrated in Kelvin, as the user is not required to subtract a large constant voltage from its output to obtain convenient Centigrade scaling. The LM35 does not require any external calibration or trimming to provide typical accuracies of 1.4C at room temperature and 3.4C over a full -55 to +150C

24temperature range. Low cost is assured by trimming and calibration at the wafer level. The LM35s low output impedance, linear output, and precise inherent calibration make interfacing to readout or control circuitry especially easy. It can be used with single power supplies, or with plus and minus supplies. As it draws only 60 A from its supply, it has very low self-heating, less than 0.1C in still air. The LM35 is rated to operate over a -55 to +150C temperature range, while the LM35C is rated for a -40 to +110C range (-10 with improved accuracy). The LM35 series is available packaged in hermetic TO-46 transistor packages, while the LM35C, LM35CA, and LM35D are also available in the plastic TO-92 transistor package. Fig. 4 shows the typical use of IC temperature sensor in the green house control system using AVR microcontroller. VI. DESIGN OBJECTIVES

Fig.2. Typical use of IC Temperature Sensor B. HUMIDITY SENSOR (HIH-3610 SERIES) Following are the features of humidity sensor selected for this design. a) Linear voltage output vs %RH b) Chemically resistant (output is not disturbed due to the presence of chemicals in the air). c) The HIH-3610 Series humidity sensor is designed specifically for high volume OEM (Original Equipment Manufacturer) users. d) Direct input to a controller due to sensors linear voltage output. Table III shows the available humidity sensors for green house application. Table III. Available Humidity Sensors for green house applications

The horticulturists near Nasik region felt the need of some automatic controller for their green houses where they grow export quality roses. The atmosphere in India change with great variance with the season. Hence, the quality of the roses does not remain the same due to the great change in the temperature and humidity parameters. Roses with adverse quality give less income. The loss in the income due to adverse quality roses is to the tune of 2 to 3 lakhs per acre per season. For roses, ideally, the green house should provide good light throughout the year, temperature range between 15 to 28C, night temperature should be between 15 to 18C, and the day temperature should not exceed 30C in any case. The growth is slowed down with the fall of temperature below 15C. If the temperature rises above 28C, humidity must be kept high. Higher night temperature above 15C hastens flower development, while lower temperature around 13.5C delays it. Depending on the temperature inside the greenhouse, the moisture should be kept in line for the best results. For example, if the temperature is 24 degrees, 60% humidity is suitable. Hence, variable temperature and humidity control for different crops using wireless technique for WSN environment using low cost technique was the main objective. Low power consumption during testing was another objective. Hence, selection of the sensors and most importantly, microcontroller, was very important keeping power consumption at remote places in view. To bring the temperature within control limit, exhaust fans were made automatically ON and for humidity control, water pump was made ON-OFF. VII. PROGRAMMING

Embedded C is used for the programming. Fig. 5 shows the the programming window of the AVR studio software used during programming.

25IX. Results

Fig.3. AVR studio window during programming Following are some important features of the AVR studio. A) Integrated Development Environment (Write, Compile and Debug) B) Fully Symbolic Source-level Debugger C) Extensive Program Flow Control Options D) Language support: C, Pascal, BASIC, and Assembly VIII. FIELD OBSERVATIONS

Results are found to be satisfactory. Area A and B (each admeasuring 10 meters x 10 meters) were selected. Area A is used to take reading without temperature and humidity control. Readings in Area B were taken after suitable automatic control action with the help of AVR based green house controller. It is found that the designed hardware has shown consistently faithful readings and also proved to be accurate in the humid atmosphere of the green house. Following readings and graphs show some of the readings in Area A and corresponding readings after corrective action in Area B. Table IV. Readings taken in the green house near Nasik before and after control action. Fan is automatically ON after temperature in AREA is more than 30 C and Motor is ON after R. Humidity is less than 50%.

Readings were taken for 15 days. ON-OFF action of the hardware was tested. Satisfactory results were achieved. Fig. 6, 7, 8 and 9 show the photographs of the green house structures used to take readings.

Fig. 5: Green house parameters in AREA A (without control action) Fig. 4: Green house near Nasik growing roses: Complete sections of the green are seen. Vents are used to regulate the temperature, naturally.

26range of 4-5 lakhs per acre. REFERENCES Network, European Journal of Scientific Research ISSN 1450-216X , Vol.33 No.2 (2009), pp.249-260 Euro Journals Publishing, Inc. 2009. [2] The Greenhouse Remote Monitoring System based on RCM2100, WANG Juan WANG Yan College of Mechanical and Electric Engineering, Agricultural University of Hebei, Baoding,071001, China [email protected]. [3] A Study on the Greenhouse Auto Control System Based on Wireless Sensor Network, BeomJin Kang Dae, Heon Park, KyungRyung Cho, ChangSun Shin, SungEon Cho , JangWoo Park IEEE, 22 December 2008. [4] Anil Kumar Singh, Precision farming Water technology center, New Delhii [5] Debashis Mandal and S. K. Ghosh, Precision Farming [6] H. J. Hellebrand, H. Beuche, K.H. Dammer, Precision Agriculture [7] S. M. Swinton and J. Lowenbergdeboer, Precision Agriculture. [8] Mahmoud Omid , A Computer-Based Monitoring System to Maintain Optimum Air Temperature and Relative Humidity in Greenhouses [9] Teemu Ahonen, Reino Virrankoski and Mohammed Elmusrati, Greenhouse Monitoring with Wireless Sensor Network [10] Andrzej Pawlowski, Jose Luis Guzman, Francisco Rodrguez, Manuel Berenguel, Jos Snchez and Sebastin Dormido, Simulation of Greenhouse Climate Monitoring and Control with Wireless Sensor Network and Event-Based Control [11] Candido, F. Cicirelli, A. Furfaro, and L. Nigro, Embedded real-time system for climate control in a complex greenhouse[1]

Fig. 6: Green house parameters in AREA B (with control action). Humidity values are increased and temperature values are decreased due to automatic control action of AVR based wireless green house controller.

X.

CONCLUSION

A. Low cost and maintenance free sensors are used to monitor environment. The system has several advantages in term of its compact size, low cost and high accuracy. B. The green house system considers design optimization and functional improvement of the system. C. The same system can be used to monitor industrial parameters also. D. The system developed has shown consistency, accuracy and precise control action over a period of 15 days and did not fail even once during testing.. E. Quality of roses in area B found to good than area A. F. Owner of the green house said that the good quality roses are sold at 1.5 times higher rate than medium quality roses. Hence, the system, if implemented, can increase the profit margin. G. The cost of the system is less than Rs. 2500/- if produced in multiple. H. For one acre green house, we need only 5 sets of AVR based green house controllers. I. Projected increase in the profit is in the

27

Fuzzy C- Mean Algorithm Using Different Variants

Abstract Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be the process of organizing objects into groups whose members are similar in some way. A cluster is therefore a collection of objects which are similar between them and are dissimilar to the objects belonging to other clusters. A group of the same or similar elements gathered or occurring closely together a bunch: Clustering of data is a method by which large sets of data is grouped into clusters of smaller sets of similar data So, the goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method is frequently used in pattern recognition. It is based on minimization of the following objective function: Index Terms clustering analysis, fuzzy clustering, fuzzy c- mean, genetic algorithm.

XIII. INTRODUCTION

C

lustering of data is a method by which large sets of data is grouped into clusters of smaller sets of similar data So, the goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. But how to decide what constitutes a good clustering? It can be shown that there is no absolute best criterion which would be independent of the final aim of the clustering. Consequently, it is the user which must supply this criterion, in such a way that the result of the clustering will suit their needs. For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding natural clusters and describe their unknown properties (natural data types), in finding useful and suitable groupings (useful data classes) or in

finding unusual data objects (outlier detection The main requirements that a clustering algorithm should satisfy are:scalability;dealing with different types of attributes; discovering clusters with arbitrary shape; minimal requirements for domain knowledge to determine input parameters; ability to deal with noise and outliers; insensitivity to order of input records; high dimensionality; interpretability and usability There are a number of problems with clustering. Among them: Current clustering techniques do not address all the requirements adequately (and concurrently); Dealing with large number of dimensions and large number of data items can be problematic because of time complexity; The effectiveness of the method depends on the definition of distance (for distance-based clustering);If an obvious distance measure doesnt exist we must define it, which is not always easy, especially in multi-dimensional spaces; The result of the clustering algorithm (that in many cases can be arbitrary itself) can be interpreted in different ways Clustering algorithms may be classified as listed below: Exclusive Clustering Overlapping Clustering Hierarchical Clustering Probabilistic Clustering

In the first case data are grouped in an exclusive way, so that if a certain datum belongs to a definite cluster then it could not be included in another cluster. A simple example of that is shown in the figure below, where the separation of points is achieved by a straight line on a bi-dimensional plane. On the contrary the second type, the overlapping clustering, uses fuzzy sets to cluster data, so that each

28point may belong to two or more clusters with different degrees of membership. In this case, data will be associated to an appropriate membership value. Instead, a hierarchical clustering algorithm is based on the union between the two nearest clusters. The beginning condition is realized by setting every datum as a cluster. After a few iterations it reaches the final clusters. Finally, the last kinds of clustering use a completely probabilistic approach Clustering algorithms can be applied in many fields, for :Marketing: finding groups of customers with similar behavior given a large database of customer data containing their properties and past buying records; Biology: classification of plants and animals given their features; Libraries: book ordering; Insurance: identifying groups of motor insurance policy holders with a high average claim cost; identifying frauds; City-planning: identifying groups of houses according to their house type, value and geographical location; Earthquake studies: clustering observed earthquake epicenters to identify dangerous zones; WWW: document classification; clustering weblog data to discover groups of similar access patterns with the update of membership uij and the cluster centers cj by:

(2)

(3) This iteration will stop when we generate the objective , where is a termination criterion between 0 and 1, whereas k are the iteration steps. This procedure converges to a local minimum or a saddle point. As already told, data are bound to each cluster by means of a Membership Function, which represents the fuzzy behaviour of this algorithm. To do that, we simply have to build an appropriate matrix named U whose factors are numbers between 0 and 1, and represent the degree of membership between data and centers of clusters of Jm.the cluster, and ||*|| is any norm expressing the similarity between any measured data and the center. Fuzzy partitioning is carried out through an iterative optimization of the objective function shown above, with the update of membership uij and the cluster centers cj Then create the algorithm of fuzzy c-mean for the next form of conversion in the search form to iteration the step of algorithm. The algorithm is composed of the following steps:Initialize U=[uij] matrix, U(0) At k-step: calculate the centers vectors C(k)=[cj] with U(k)

XIV. FUZZY C-MEANS CLUSTERING ALGORITHM Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method (developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition. It is based on minimization of the following objective function:

(1) where m is any real number greater than 1, uij is the degree of membership of xi in the cluster j, xi is the ith of d-dimensional measured data, cj is the ddimension center of the cluster, and ||*|| is any norm expressing the similarity between any measured data and the center. Fuzzy partitioning is carried out through an iterative optimization of the objective function shown above,

Using 1 and 2 Update U(k) , U(k+1)

29choosing an initialization for the c-means clustering algorithms. Experiments use six data sets, including the Iris data, magnetic resonance and color images. The genetic algorithm approach is generally able to find the lowest On data sets with several local extreme, the GA approach always avoids the less desirable solutions. Deteriorate partitions are always avoided by the GA approach, which provides an effective method for optimizing clustering models whose objective function can be represented in terms of cluster centers. The time cost of genetic guided clustering is shown to make series of random initializations of fuzzy/hard c-means, where the partition associated with the lowest J value is chosen, and an effective competitor for many clustering domains. The subtractive clustering method assumes each data point is a potential cluster center and calculates a measure of the likelihood that each data point would define the cluster center, based on the density of surrounding data points.

If || U(k+1) - U(k)||< step 2.

then STOP; otherwise return to

Advantage:-i). Gives best result for overlapped data set and comparatively better than k-mean algorithm.

XV. VARIANTS IN FUZZY C-MEAN The most widely used clustering algorithm implementing the fuzzy philosophy is Fuzzy CMeans (FCM), initially developed by Dunn and later generalized by Bezdek, who proposed a generalization by means of a family of objective functions . Despite this algorithm proved to be less accurate than others, its fuzzy nature and the ease of implementation made it very attractive for a lot of researchers, that proposed various improvements and applications refer to . Usually FCM is applied to unsupervised clustering problems. i) Optimizing of Fuzzy C-Means Clus