Development of Swarm Intelligent Systems for MANET

Transcription

1 Development of Swarm Intelligent Systems for MANET By Sharvani G S Supervisor Dr. T. M. Rangaswamy A Thesis submitted to Avinashilingam University for Women, Coimbatore-43 In partial fulfilment of the requirements for the award of the degree of Doctor of Philosophy in Computer Science and Engineering OCTOBER 2012

2 Declaration I hereby declare that the thesis entitled Development of Swarm Intelligent Systems for MANET, submitted to the Department of Computer Science and Engineering, Faculty of Engineering, Avinashilingam University for Women, Coimbatore, for the award of Doctor of Philosophy in Computer Science and Engineering is a record of original research work carried out by me under the supervision and guidance of Dr T. M. Rangaswamy, Professor, R V College of Engineering, Bangalore, Karnataka, India and it has not formed the basis for the award of any Degree / Diploma / Associate ship / Fellowship or any other similar title to any candidate of any university. Signature of the Candidate

3 Certificate This is to certify that the dissertation entitled Development of Swarm Intelligent Systems for MANET, submitted to the Department of Computer Science and Engineering, Faculty of Engineering, Avinashilingam University for Women, Coimbatore, for the award of Doctor of Philosophy in Computer Science and Engineering is a record of original research work carried out by Sharvani G.S, during the period of her study (April 2009 October 2012) in the Department of Computer Science and Engineering, Faculty of Engineering, Avinashilingam University for Women, Coimbatore, under my supervision and guidance and the thesis has not formed the basis for the award of any Degree / Diploma / Associate ship / Fellowship or any other similar title to any candidate of any university. Signature of the Guide

4 ACKNOWLEDGEMENT I would like to thank my guide Dr. T M Rangaswamy for his encouragement and thoughtful guidance. With his guidance and invaluable suggestions this research work could be made successful. I greatly acknowledge the Revered Chancellor, Vice-Chancellor for their encouragement and support during this research work. I am deeply indebted to Dr Ananth A G, Professor, R.V.College of Engineering and Dr. S. C. Sharma, Vice Chancellor, Tumkur University for constant inspiration, strength and moral support. I wholeheartedly thank Dr S Satyanarayana, Principal, R.V.College of Engineering, for his support and encouragement. I would also like to express my thanks to Dr. N K Srinath, and Dr N K Cauvery, for providing the required facilities and moral support during my research work. I thank the management of Rashtreeya Shikshana Samithi Trust (RSST) for their unstinted cooperation and encouragement to carry out my PhD work. I owe everything to my husband. Without his endless love and support I wouldn t have finished this work. I am grateful to my son, my parents, my sister-in-law and my father-in-law for supporting me in every aspect. I sincerely thank all my friends, colleagues and all people who have supported me directly or indirectly in carrying out my research work and preparation of the thesis. Sharvani G.S i

5 ABSTRACT Mobile Adhoc Network (MANET) is an infrastructure-less multi hop network built dynamically for temporary connections. It is suitable for applications such as target tracking, search and rescue operation, disaster relief, military applications etc. However, the MANET architecture poses many challenges due to its dynamic topology, mobility, error prone shared channel etc. Many protocols were developed which aimed at optimizing the routes for data transmission while an attempt was made to retain small message overhead and maximize the network lifetime. It is found that the Swarm Intelligence (SI) inspired algorithms such as Ant Colony Optimization (ACO) are better suited for highly adaptive networks like MANETs. Biological ants at the time of food foraging, navigate their chosen path and deposit a chemical called pheromone on the ground, there by establishing the trail. Thickness of the trail attracts other ants to follow the path to reach the food source. Analogous to this, packets in the networks are biased towards the highest pheromone value for its destination while leaving a trail to its source. Laying a source pheromone along the same trail increases the possibility of packets travelling the same path in the reverse order to the source. To remove stale entries from the network, pheromone decreases using decay techniques. Pheromone tuning has to be balanced in such a way that stale entries are not retained and at the same time, good paths are not lost. The principles of ACO are used for each packet flow in MANETs, resulting in emergent routing behavior. Other additional benefits achieved from ACO are reduced control overhead and efficient route maintenance. One of the major problems with ACO is stagnation. This occurs when all ants try to follow the same path to reach the destination (since there is more pheromone). This causes congestion when applied in MANETs. In this case, simple implementations of ant algorithms for MANET are not sufficient and some modifications in pheromone trails have to be made in order to balance the deposit / decay of pheromone trails. ii

6 To circumvent stagnation, Pheromone Control technique is adopted. There are different ways to control the pheromone namely evaporation, aging, limiting and smoothing pheromone. This research work focuses on evaporation as a Pheromone control technique which avoids non-optimal paths formed due to stagnation. Present investigations include study of various Pheromone decay models like Discrete, Polynomial and Exponentiation Decay Models. The detailed analysis carried out indicates that the Exponential Decay Model for the pheromone works better than the Polynomial and Discrete Decay Models. This can be attributed to the large traces of pheromone trails left which take longer time for selecting the best path. The exponential decay model is proven to be better in terms of load distribution. However, Exponential decay technique loses pheromone traces very fast leading to loss of good solutions in MANETs. A new technique for Exponential Decay Model which evaporates pheromone trails in a controlled manner is implemented. This technique considers node stability of the neighboring node for fine tuning the pheromone concentration. The stability of the node is calculated based on Node Stability Factor. Where is the ratio between the hello sent and hello replied by a node to its neighbors. The Node Stability Factor indicates the link stability in relation to the other paths towards the destination. A higher ratio indicates that the neighbor node is more stable as compared to those with a lesser ratio. Using this concept the evaporation of pheromone is fine- tuned i.e. pheromone is decayed faster for less stable nodes, whereas pheromone decay is slower for stable nodes. This helps in faster decrease of the pheromone content of faulty path. By adopting efficient pheromone evaporation techniques, Modified Termite Algorithm (MTA) has been developed and implemented on the MANET. The implemented MTA is found to improve the network performance in terms of throughput, end-to-end delay and routing overheads. Further the implemented MTA on MANET also address QoS with efficient route maintenance by local route repair strategy, which is observed to enhance the performance of the network in terms of throughput, end to end delay and routing overheads. iii

7 TABLE OF CONTENTS ACKNOWLEDGMENT ABSTRACT TABLE OF CONTENTS INTERNATIONAL PUBLICATIONS LIST OF FIGURES LIST OF TABLES i ii iv viii xi xiii CHAPTER 1 INTRODUCTION Wireless Networks Ad-Hoc Wireless Networks Major challenges of MANETs Routing in MANETs Quality of Service (QoS) Social Insects and stigmergy Swarm Intelligence Basic principle of Swarm Intelligence Swarm Intelligent Framework Applications of Ant colony Approach for networks Major categories of SI algorithms Types of Swarm Intelligence based ACO algorithms for routing 18 CHAPTER 2 PROBLEM STATEMENT AND OBJECTIVES Motivation Problem Statement Objectives Organization of Thesis 23 CHAPTER 3 iv

8 LITERATURE SURVEY Biologically inspired routing algorithms GPS based routing using ACO Stagnation avoidance algorithms QoS in MANETs using ACO Local route repair strategy using prediction 36 CHAPTER 4 ANALYSIS OF PHEROMONE DECAY TECHNIQUES FOR ACO BASED 37 ROUTING 4.1 Design of the algorithm Structure chart for pheromone update Efficient fine tuning pheromone technique to alleviate stagnation problem Results and Discussion 44 CHAPTER 5 FINE TUNING OF PHEROMONE CONCENTRATION FOR MODIFIED 55 TERMITE ALGORITHM (MTA) 5.1 Termite Hill Building process Modified Termite Algorithm (MTA) Efficient stagnation avoidance technique Results and Discussion 63 CHAPTER 6 MTA IMPLEMENTATION ON MANET WIT QOS SPECIFIED 69 EFFICIENT LOCAL ROUTE REPAIR STRATEGY 6.1 Modified Termite Algorithm (MTA) with QoS Efficient route maintenance by predictive preemptive local route repair 71 strategy 6.3 Results and discussion 76 v

9 CHAPTER 7 DEVELOPMENT OF MTA AND MANET IMPLEMENTATION Structured Chart of Modified Termite Algorithm Component Design Pheromone Updation Implementation 98 CHAPTER 8 CONCLUSION AND FUTURE WORK Conclusion Future work 103 REFERNCES 105 GLOSSARY 123 APPENDIX-A 124 APPENDIX-B 126 vi

10 INTERNATIONAL PUBLICATIONS INTERNATIONAL JOURNALS 1. Sharvani G S, Dr. A G Ananth and Dr T M Rangaswamy, Analysis of different pheromone decay techniques for ACO based routing in ad hoc wireless networks, 2012 (Accepted by International Journal of Computer Applications with Impact factor 0.814). 2. Sharvani G S, Dr. A G Ananth and Dr T M Rangaswamy, Efficient stagnation avoidance for MANETs with local repair strategy using Ant Colony Optimization, International Journal of Distributed and Parallel Systems (IJDPS), Vol 3, No 5, pp , September Sharvani G S, Dr. A G Ananth and Dr T M Rangaswamy, Ant Colony Optimization based Modified Termite Algorithm (MTA) with efficient stagnation avoidance strategy for MANETs, International Journal on Applications of Graph Theory in Wireless Ad hoc Networks and Sensor Networks (GRAPH-HOC) Vol 4, No 2/3, pp 39-50, September Sharvani G S, Dr T M Rangaswamy, Efficient Pheromone Adjustment Techniques in ACO for Ad Hoc Wireless Network Intl Journal of Computer Applications Vol 44-No 6 Pp 29-32, April 2012 (impact factor.0.835). 5. G.R. Smitha, G.S. Sharvani and Dr. T M Rangaswamy, QoS-Novel Multipath Routing Protocol for Mobile Ad-Hoc Networks, Journal of Wireless Communication, CIIT Online: ISSN Vol 3 No 10, Pp , (impact factor 0.572). 6. Sharvani.G.S, T.M.Rangaswamy, Efficient Packet Delivery approach for Adhoc wireless networks, International Journal on CS&IT vii

11 CSCP/vol1/cscp0104 DOI: /csit , Pp (Cited by 1). 7. Sharvani.G.S, T.M.Rangaswamy, Development of swarm Intelligent systems for MANETs, International Journal on Recent Trends in Engineering & Technology, Vol. 05, No. 01, ACEEE,, Pp ,Mar Sharvani GS, Dr.T.M.Rangaswamy, Sudarshan M H, Keerthi Kiran H P,Pavankumar V V and Thandeep G B, Resource reservation using bandwidth in MANETS for swarm intelligence algorithm: Termite, Journal of Telecommunications, Volume 3, ISSUE 2, ISSN , PP 54-58, July Sharvani.G.S, N.K.Cauvery, T.M.Rangaswamy, Adaptive routing algorithm for MANETs: Termite, I International Journal of Next-Generation Networks (IJNGN),Vol.1, No.1, ISSN : (online), ISSN : ( Print ), Pp 38-43, December 2009, (Cited by 3). 10. Sharvani.G.S, N.K.Cauvery, Types of MANETs: A Survey, CIIT International Journal of Wireless Communication Print: ISSN & Online : ISSN , Pp May (impact factor 0.572). INTERNATIONAL CONFERENCES 1. Sharvani G S, Vinay Kumar Kolli, An ACO-Based Efficient Stagnation Avoidance Methodology for MANETS R. Maringanti et al. (eds.), Proceedings of Ninth International Conference on Wireless Communication and Sensor Networks, IIIT Allahabad, Lecture Notes in Electrical Engineering 299, Pp , December-2013 DOI: / _12, Springer India Sharvani.G.S, Dr.T.M.Rangaswamy, Aayush Goel, Ajith B, Binod Kumar and Manish Kumar, Providing Qos using Predictive Premptive based Local viii

12 route strategy, International Conference on Computer Science and Information Technology COSIT Proceeding Published by Springer LNCS Book Chapter Advances in Networks and communications ISSN ), Pp 55-61,Jan Sharvani.G.S, Dr.T.M.Rangaswamy, N.K Cauvery, Bandwidth Efficient routing in Swarm Intelligence, ICISD 2011 Vallabha Vidyanagar, Gujarat,ISBN No , Pp 29-33, Jan Sharvani.G.S, Dr.T.M. Rangaswamy, QoS Methodologies in MANETs, International Conference on Computer Applications ICCA-2010, Pondicherry, Pp , Dec Sharvani.G.S, N.K.Cauvery, T.M.Rangaswamy, Different types of swarm Intelligence algorithms for routing, International conference on Advances in Recent Technologies in Communication and computing(artcom) Pp ,Oct-2009, Kottyam, Kerala,ISBN: Proceedingspublished and Indexed by IEEE explorer. (Cited by 5). ix

13 LIST OF FIGURES Fig 1.1 Cellular networks 2 Fig 1.2 Adhoc Wireless Networks 2 Fig 1.3 QoS in MANETS with Delay as a parameter 7 Fig 1.4 Cross Layer Architecture 9 Fig 1.5 Schematic diagram of termite nest 10 Fig 1.6 Food foraging behavior in ants [13] 11 Fig 4.1 Structured chart for Pheromone Update. 41 Fig 4.2 (a) Pheromone intensity vs. period (b) Probability of path selection vs period Fig 4.3 (a) Pheromone intensity vs. period (b) Probability of path selection vs. period Fig 4.4 (a) Pheromone intensity vs period (b) Probability of path selection vs period Fig 4.5 Analysis of different decay techniques 49 Fig 4.6 (a) Pheromone intensity vs period (b) Probability of path selection vs period Fig 4.7(a) Pheromone intensity vs period (b) Probability of path selection vs period Fig 4.8 PDR versus Combined view of decay rates 53 Fig 5.1 Flowchart of Termite Hill building process 56 Fig 5.2 Context Diagram - MANETs using MTA 57 Fig 5.3 Phases of MANETs using MTA 58 Fig 5.4 S node neighborhood 59 Fig 5.5 Data structure maintained at S node 61 Fig 5.6 Throughput Vs Packet size Analysis for 30 nodes 64 Fig 5.7 Throughput Vs Packet size Analysis for 50 nodes 64 Fig 5.8 End-to-End Delay (ms) Vs Packet size Analysis for 30 nodes 65 Fig 5.9 End to End Delay (ms) Vs Packet size Analysis for 50 nodes 66 Fig 5.10 Routing Overhead Vs Packet size Analysis for 30 nodes 67 x

14 Fig 5.11 Routing Overhead Vs Packet size Analysis for 50 nodes 68 Fig 6.1 Sequence diagram of route discovery with QoS 71 Fig 6.2 Failure Handling Module in MTA 72 Fig 6.3 Failure Recovery Module in MTA 74 Fig 6.4 Throughput Vs No of exhausted Nodes 77 Fig 6.5 Throughput Vs No of Exhausted Nodes 78 Fig 6.6 End to End Delay (ms) Vs No of Exhausted Nodes 79 Fig 6.7 End to End Delay (ms) Vs No of Exhausted Nodes 79 Fig 6.8 Routing Overhead Vs No of Exhausted Nodes 80 Fig 6.9 Routing Overhead Vs No of Exhausted Nodes 81 Fig 7.1 Structure Chart for Termite Routing System 84 Fig 7.2 Structure Chart for Route Discovery 84 Fig 7.3 Structure Chart for Route Maintenance 86 Fig 7.4 Structure Chart for Route Failure 87 Fig 7.5 Main components of the system 88 Fig 7.6 Data transfer flow representation 95 Fig 7.7 Pheromone updation 97 xi

15 LIST OF TABLES Table 3.1 Stagnation avoidance survey 30 Table 4.1 Pheromone table for node V i 40 Table 4.2 Probability of path selection for different decay techniques 48 Table 4.3 Probability of path selection for different stability factor 50 Table 5.1 Example Routing Table at Node S 59 Table 5.2 Node Stability Factor of Neighbor Nodes at Node S 62 Table 5.3 Decay Factor for Different Ratio 62 Table 7.1 Data Packet 100 Table 7.2 Route Request / Reply / DataAck 100 Table 7.3 Hello sent / Hello Reply 100 xii

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