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International Journal of Networks and Communications 2013, 3(1): 1-11 DOI: 10.5923/j.ijnc.20130301.01
Analysis of Routing Protocols in AD HOC and Sensor Wireless Networks Based on Swarm Intelligence
Zulfiqar Ali, Waseem Shahzad*
National University of Computer & Emerging Science, Islamabad, Pakistan
Abstract There are various bio inspired and evolutionary approaches including genetic programming (GP), Neural
Network, Evo lutionary programming (EP) exp loited for routing optimizat ion in MANETs and WSNs. The Swarm Intelligence based algorithmic approaches; Particle Swarm Optimization (PSO) and Ant Co lony Optimization (A CO) are more p ro mising in provid ing loop free, energy-aware, and mult i-path routing in mob ile ad hoc and wireless sensor networks. There are various constraints involved in ad hoc and sensor networks protocols due to the mobility and non infrastructure nature of the system. We study in this research work a probabilistic performance evaluation frameworks and Swarm Intelligence approaches (PSO, A CO) for routing protocols. The performance evaluation metrics employed for wireless and ad hoc routing algorith ms is , (a) routing overhead, (b) route optimality, and (c) energy consumption. Th is survey provides collection of Swarm Intelligence based algorithms for mobile ad hoc an d sensor networks and their critical analysis. The study concludes that PSO and ACO based protocols are advantageous than other approaches applied for the routing optimization in ad hoc and wireless sensor networks.
Keywords Ant Colony Optimizat ion (ACO), MANET, Honeybees, Swarm Intelligent, PSO, Wireless Sensor Network
(WSN)
1. Introduction
The MANETs are a co llect ion of wireless mob ile nodes having no well established infrastructure and dynamically changing topology in nature. Due to the frequently changes in topology and infrastructure less nature, Ad hoc networks require a h ighly adaptive routing algebraic approaches. In ad hoc net works , co mpon
Analysis of Routing Protocols in AD HOC and Sensor Wireless Networks Based on Swarm Intelligence
Zulfiqar Ali, Waseem Shahzad*
National University of Computer & Emerging Science, Islamabad, Pakistan
Abstract There are various bio inspired and evolutionary approaches including genetic programming (GP), Neural
Network, Evo lutionary programming (EP) exp loited for routing optimizat ion in MANETs and WSNs. The Swarm Intelligence based algorithmic approaches; Particle Swarm Optimization (PSO) and Ant Co lony Optimization (A CO) are more p ro mising in provid ing loop free, energy-aware, and mult i-path routing in mob ile ad hoc and wireless sensor networks. There are various constraints involved in ad hoc and sensor networks protocols due to the mobility and non infrastructure nature of the system. We study in this research work a probabilistic performance evaluation frameworks and Swarm Intelligence approaches (PSO, A CO) for routing protocols. The performance evaluation metrics employed for wireless and ad hoc routing algorith ms is , (a) routing overhead, (b) route optimality, and (c) energy consumption. Th is survey provides collection of Swarm Intelligence based algorithms for mobile ad hoc an d sensor networks and their critical analysis. The study concludes that PSO and ACO based protocols are advantageous than other approaches applied for the routing optimization in ad hoc and wireless sensor networks.
Keywords Ant Colony Optimizat ion (ACO), MANET, Honeybees, Swarm Intelligent, PSO, Wireless Sensor Network
(WSN)
1. Introduction
The MANETs are a co llect ion of wireless mob ile nodes having no well established infrastructure and dynamically changing topology in nature. Due to the frequently changes in topology and infrastructure less nature, Ad hoc networks require a h ighly adaptive routing algebraic approaches. In ad hoc net works , co mpon
References: [1] Charles E. Perkins and Elizabeth M . Royer. "Ad hoc On-Demand Distance Vector Routing." Proceedings of the 2nd IEEE Workshop on M obile Computing Systems and Applications, New Orleans, LA, pp. 90-100, February 1999. David B Johnson and David A M altz, “DSR : The Dynamic Source Routing Protocol for M ulti-Hop Wireless Ad Hoc Networks”, in Computer and Information Science, Volume: 5, pp. 1-25, 2001. T. Clausen, P.Jacquet, A Laouiti, P.M uhlethaler, A. Qayyum, and L.Viennot, “Optimized link state routing protocol for ad hoc networks,” in Proceedings of IEEE, INM IC, pp. 62 - 68, 2001. Zygmunt J. Haas and M arc R. Pearlman, “ The performance of query control schemes for the zone routing protocol”, In Proceedings of the ACM SIGCOMM '98 conference on [2] [3] [4] International Journal of Networks and Communications 2013, 3(1): 1-11 11 [18] Xun-bing Wang, Yong-zhao Zhan, Liang-min Wang and Li-ping Jiang, “Ant Colony Optimization and Ad-hoc On-demand M ultipath Distance Vector Based Routing Protocol,” IEEE Fourth International Conference on Natural Computation, pp. 589-593, 2008. [19] Roth M artin and Wicker Stephen, “Termite: ad-hoc networking with stigmergy”, in IEEE Conference, Global Telecommunications, vol.5, pp. 2937 – 2941, 2003. [20] M esut Gunes¸, Udo Sorges and Imed Bouazizi, “ARA – The Ant-Colony Based Routing Algorithm for M ANETs”, International Workshop on Ad Hoc Networking (IWAHN 2002), Vancouver, British Columbia, Canada, August 18-21, 2002. [21] Di Caro, G., Ducatelle, F. and Gambardella, L. M ., “AntHocNet: An Adaptive Nature-Inspired Algorithm for Routing in M obile Ad Hoc Networks”, European Transactions on Telecommunications (ETT), Special Issue on Self Organization in M obile Networking, Volume 16, Number 5, pp. 443-455, September-October 2005. [22] E. Baburaj and V. Vasudevan “An Intelligent On Demand M ulticast Routing Protocol for M ANETs,”, IEEE, First International Conference on Emerging Trends in Engineering and Technology, pp. 214-217, 2008. [23] O. Hussein and T. Saadawi ,“Ant Routing Algorithm for M obile Ad-hoc networks (AMMA),” , IEEE, Performance, Computing, and Communications Conference, pp.281 – 290, 2003. [24] Ping Yuan, Chunlin Ji, Yangyang Zhang and Yue Wang, “Optimal M ulticast Routing in Wireless Ad Hoc Sensor Networks” , International Conference on Networking, Sensing & Control, IEEE , 2004. [25] S. Gheitanchi, F. H. Ali, and E. Stipidis, "Trained particle swarm optimization for collaborative computing networks," in Proceedings of the Convention Communication, Interaction and Social Intelligence (AISB '08), vol. 11, pp. 7-12, Aberdeen, Scotland, 2008. [26] Okazaki, A.M and Frohlich, A.A., “Ant-based Dynamic Hop Optimization Protocol: A routing algorithm for M obile Wireless Sensor Networks”, in conference GLOBECOM Workshops (GC Wkshps), IEEE, 1139 – 1143, Dec. 2011. [27] Tiago Camilo, Carlos Carreto, Jorge SáSilva, and Fernando Boavida. “An energy-efficient ant-based routing algorithm for wireless sensor networks”, In Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence (ANTS '06), pp. 49-59, 2006. [28] Liao M ing-hua, Zhang Hua, Sun Guang, "Energy Aware Routing Algorithm for Wireless Sensor Network Based on Ant Colony Principle", JCIT: Journal of Convergence Information Technology, Vol. 7, No. 4, pp. 215 – 221, 2012. [29] Rajeshwar Singh, Dharmendra K Singh and Lalan Kumar, “Performance Evaluation of ACO Based on Demand Routing Algorithm for M obile Ad Hoc Networks”, in Internal Journal Engineering Science and Technology(IJEST), Vol.3, Issue 3, pp. 1809-1815, M arch 2011. [30] Okazaki, A.M and Frohlich, A.A., “Ant-based Dynamic Hop Optimization Protocol: A routing algorithm for M obile Wireless Sensor Networks”, in GLOBECOM Workshops (GC Wkshps), IEEE, pp. 1139 – 1143, Dec. 2011. [31] Jian-Feng Yan, Yang Gao and Lu Yang, “Ant colony optimization for wireless sensor networks routing”, International Conference on M achine Learning and Cybernetics (ICM LC), pp. 400 - 403 , July 2011. [32] Xie Hui, Zhang Zhigang and Zhou Xueguang, “A Novel Routing Protocol in Wireless Sensor Networks Based on Ant Colony Optimization”, on International Conference, Environmental Science and Information Application Technology(ESIAT), pp. 646 – 649, July 2009.