Title: Evolving a clustering algorithm for wireless sensor network using particle swarm optimisation
Authors: Basma Solaiman; Alaa F. Sheta
Addresses: Computer Science Department, College of Computer Science and Information Technology, Sudan University of Science and Technology (SUST), P.O. Box 295119, Khartoum, Sudan ' Computers and Systems Department, Electronics Research Institute (ERI), El-Tahrir Street, Dokky, Giza 12622, Egypt
Abstract: Energy consumption is a vital problem that faces wireless sensor network (WSN) because sensor nodes are always equipped with batteries that cannot be recharged or replaced. Thus, maximising the lifetime of WSN by means of minimising the energy dissipation is an essential aspect in WSN deployment. In this paper, we propose a novel algorithm to cluster the WSN using particle swarm optimisation, named PSO-VC. The proposed algorithm is designed to obtain the optimal number of clusters, optimum cluster heads and optimum clusters layout. The proposed PSO-VC aimed to maximise the number of transmissions which a CH can perform before the node depletes its energy. Our proposed algorithm was evaluated and compared with traditional Low-Energy Adaptive Clustering Hierarchy (LEACH) clustering protocol. Moreover, the same algorithm is re-implemented using genetic algorithm, named GA-VC. It was found that the proposed PSO-VC preserves more energy and considerably prolongs the network lifetime compared to the GA-VC and LEACH algorithms.
Keywords: wireless sensor networks; WSNs; clustering algorithms; particle swarm optimisation; PSO; genetic algorithms; evolutionary computation; energy consumption; network lifetime; LEACH.
International Journal of Swarm Intelligence, 2016 Vol.2 No.1, pp.43 - 65
Received: 12 Dec 2014
Accepted: 13 Jul 2015
Published online: 30 Jun 2016 *