An adaptive clustering algorithm based on improved particle swarm optimisation in wireless sensor networks Online publication date: Tue, 03-Nov-2015
by Deng-ao Li; Hailong Hao; Guolong Ji; Jumin Zhao
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 8, No. 4, 2015
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.
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