Publication Type:

Conference Paper


Proceedings of the International Conference on Advances in Computing, Communications and Informatics, ACM, p.367-373 (2012)



Comprehensive Learning Particle Swarm Optimization (CLPSO), Localization, Particle swarm optimization (PSO), Wireless sensor networks (WSN)


Wireless sensor network (WSN) refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. Sensor Localization is a fundamental challenge in WSN. In this paper localization is modeled as a multi dimensional optimization problem. A comparison study of energy of processing and transmission in a wireless node is done, main inference made is that transmission process consumes more than processing. An energy efficient distributed localization technique is proposed. Distributive localization is addressed using swarm techniques Particle Swarm Optimization (PSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) because of their quick convergence to quality solutions. The performances of both algorithms are studied. The accuracy of both algorithms is analyzed using parameters such as number of nodes localized, computational time and localization error. A simulation was conducted for 100 target nodes and 20 beacon nodes, the results show that the PSO based localization is faster and CLPSO is more accurate.


cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@50012b0a ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@7cb0c68e Through org.apache.xalan.xsltc.dom.DOMAdapter@39bbd496; Conference Code:92570

Cite this Research Publication

Dr. Maneesha V. Ramesh, Divya, P. L., Kulkarni, R. V., and Rekha, P., “A Swarm Intelligence Based Distributed Localization Technique For Wireless Sensor Network”, in Proceedings of the International Conference on Advances in Computing, Communications and Informatics, 2012, pp. 367-373.

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