An adaptive clustering algorithm based on improved particle swarm optimisation in wireless sensor networks. (2015)

• Record Type: Journal Article
• Authors: Li, Deng-ao; Hao, Hailong; Ji, Guolong; Zhao, Jumin
• Is Part Of: International journal of high performance computing and networking. Volume 8:Number 4(2015)
• Page Start: 370
• Page End: 380
• Publication Date: 2015
• Subjects: wireless sensor networks -- WSNs -- network lifespan -- particle swarm optimisation -- nonlinear PSO -- dynamic PSO -- adaptive PSO -- energy-aware clusters -- cluster heads -- proportion -- energy consumption equilibrium -- competition mechanism -- evaluation -- fitness function -- adaptive clustering -- intra-cluster distance -- simulation; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

Abstract

In wireless sensor networks (WSNs), the lifetime of networks is a critical issue. In order to expand the lifespan of WSNs, an efficient technique is need to reduce energy consumption of WSNs. In this paper, an improved nonlinear dynamic adaptive particle swarm optimisation (NDAPSO) is applied for producing energy-aware clusters with selection of optimal cluster heads. The fitness function used for evaluating the particles consider four features, such as energy consumption, intra-cluster distance, the proportion of cluster heads energy and degree of energy consumption equilibrium. And a new cluster head competition mechanism is introduced in this paper. Then in the process of NDAPSO, the optimal cluster heads are selected by comparing quality of particles, which is evaluated by the fitness function. The simulation results show that this algorithm effectively reduces the communication energy consumption and improves the networks lifespan.