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Dynamic clustering using binary multi-objective particle swarm optimization for wireless sensor networks

Lookup NU author(s): Professor Harris Tsimenidis, Professor Bayan Sharif, Dr Cassim Ladha


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In wireless sensor networks, the use of energy efficient infrastructure such as clustering may be used to lengthen the network lifetime and prevent network connectivity degradation. In such systems, the performance of the clustering scheme is generally influenced by the cluster head selection method and the number of clusters. This paper presents a dynamic clustering method with multi-objectives that automatically determines the optimum number of clusters in the network. The algorithm, which is based on binary Particle Swarm Optimization (PSO), eliminates the need to set the number of clusters a priori. In addition, a multi-objective approach is utilized in the cluster head selection algorithm in order to select the best set of cluster heads. Simulation results demonstrate that the proposed protocol can achieve an optimal number of clusters, as well as prolong the network lifetime and increase the data delivery at the base station when compared to other well known clustering algorithms. © 2008 IEEE.

Publication metadata

Author(s): Latiff N, Tsimenidis C, Sharif B, Ladha C

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

Year of Conference: 2008

Pages: 5pp

Publisher: IEEE


DOI: 10.1109/PIMRC.2008.4699768

Library holdings: Search Newcastle University Library for this item

ISBN: 9781424426447