Computational Intelligence Approaches for Energy Optimization in Wireless Sensor Networks

Wireless sensor networking is a promising technology that can lead to automatic, intelligent, easier and more secure systems. A wireless sensor network (WSN) consists of small battery powered devices with limited energy resources. One of the major challenges in WSN lies in the energy constraint and computation resources available at the sensor nodes. One way to achieve energy efficiency would be through the use of a clustering technique. In this paper, we propose computational intelligence (CI) approaches to deal with the problem of sensor nodes clustering in a WSN with the ultimate goal to reduce energy expenditures and thus to extend the lifetime of the network. The main motivation is that CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures, and scenario changes. The main features of the proposed work span over two aspects. First, four metaheuristics have been adapted to deal with the tackled problem namely genetic algorithms, evolution strat...

[1]  M. F. Fuller,et al.  Practical Nonparametric Statistics; Nonparametric Statistical Inference , 1973 .

[2]  R. Eberhart,et al.  Particle Swarm Optimization-Neural Networks, 1995. Proceedings., IEEE International Conference on , 2004 .

[3]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[4]  Prasanta K. Jana,et al.  Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach , 2014, Eng. Appl. Artif. Intell..

[5]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[6]  Alaa F. Sheta,et al.  Computational Intelligence for Wireless Sensor Networks: Applications and Clustering Algorithms , 2013 .

[7]  Madhusudhanan Baskaran,et al.  Synchronous Firefly Algorithm for Cluster Head Selection in WSN , 2015, TheScientificWorldJournal.

[8]  K. Murugan,et al.  Energy aware optimal cluster head selection in wireless sensor networks , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[9]  Xiaohui Yuan,et al.  Evolution strategies based image registration via feature matching , 2004, Inf. Fusion.

[10]  Rafael S. Parpinelli,et al.  New inspirations in swarm intelligence: a survey , 2011, Int. J. Bio Inspired Comput..

[11]  Nidal Nasser,et al.  Secure Multipath Routing Protocol for Wireless Sensor Networks , 2007, 27th International Conference on Distributed Computing Systems Workshops (ICDCSW'07).

[12]  Zulfiqar Ali,et al.  Analysis of Routing Protocols in AD HOC and Sensor Wireless Networks Based on Swarm Intelligence , 2013 .

[13]  Ganesh K. Venayagamoorthy,et al.  Computational Intelligence in Wireless Sensor Networks: A Survey , 2011, IEEE Communications Surveys & Tutorials.

[14]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[15]  Manish Dixit,et al.  An Exhaustive Survey on Nature Inspired Optimization Algorithms , 2014 .

[16]  Abbas Karimi,et al.  Cluster head selection using fuzzy logic and chaotic based genetic algorithm in wireless sensor network , 2013 .

[17]  Karunya Nagar.  Optimizing Energy in WSN using Evolutionary Algorithm , 2011 .

[18]  Yaduvir Singh,et al.  Genetic Algorithms: Concepts, Design for Optimization of Process Controllers , 2011, Comput. Inf. Sci..

[19]  Selcuk Okdem,et al.  Cluster based wireless sensor network routing using artificial bee colony algorithm , 2012, Wirel. Networks.

[20]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[21]  R. Poli.  An Analysis of Publications on Particle Swarm Optimisation Applications , 2007 .

[22]  John H. Holland,et al.  Genetic Algorithms and the Optimal Allocation of Trials , 1973, SIAM J. Comput..

[23]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[24]  Annie S. Wu,et al.  Sensor Network Optimization Using a Genetic Algorithm , 2003 .

[25]  Moslem Afrashteh Mehr,et al.  Design and Implementation a New Energy Efficient Clustering Algorithm using Genetic Algorithm for Wireless Sensor Networks , 2011 .

[26]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[27]  Qing Zhao,et al.  On the lifetime of wireless sensor networks , 2005, IEEE Communications Letters.

[28]  Charalampos Tsimenidis,et al.  Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.