Using Multi-Objective Particle Swarm Optimization for Energy-Efficient Clustering in Wireless Sensor Networks

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Author(s): Hamid Ali (National University of Computer and Emerging Sciences, Pakistan), Waseem Shahzad (National University of Computer and Emerging Sciences, Pakistan)and Farrukh Aslam Khan (National University of Computer and Emerging Sciences, Pakistan)
Copyright: 2012
Pages: 14
Source title: Wireless Sensor Networks and Energy Efficiency: Protocols, Routing and Management
Source Author(s)/Editor(s): Noor Zaman (King Faisal University, Saudi Arabia), Khaled Ragab (King Faisal University, Saudi Arabia)and Azween Bin Abdullah (Universiti Teknologi Petronas, Malaysia)
DOI: 10.4018/978-1-4666-0101-7.ch013

Keywords: Information Science Reference / Media & Communications / Mobile and Wireless Computing / Wireless Systems

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Abstract

In this chapter, the authors propose a multi-objective solution to the problem by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in a sensor network in order to provide an energy-efficient solution. The proposed algorithm considers the ideal degree of nodes and battery power consumption of the sensor nodes. The main advantage of the proposed method is that it provides a set of solutions at a time. The results of the proposed approach were compared with two other well-known clustering techniques: WCA and CLPSO-based clustering. Extensive simulations were performed to show that the proposed approach is an effective approach for clustering in WSN environments and performs better than the other two approaches.

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Using Multi-Objective Particle Swarm Optimization for Energy-Efficient Clustering in Wireless Sensor Networks

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