An adaptive clustering algorithm based on improved particle swarm optimisation in wireless sensor networks

被引：1

作者：

Li, Deng-Ao ^{[1
]}

Hao, Hailong ^{[1
]}

Ji, Guolong ^{[2
]}

Zhao, Jumin ^{[1
]}

机构：

[1] College of Information Engineering, Taiyuan University of Technology, 79 West Yingze Street, Taiyuan, Shanxi Province

[2] State Grid Shanxi Electric Power Research Institute, 6 Qingnian Road, Taiyuan, Shanxi Province

来源：

International Journal of High Performance Computing and Networking | 2015年 / 8卷 / 04期

关键词：

Cluster head; Competition mechanism; Energy consumption equilibrium; Energy-aware; Evaluating; Fitness function; Lifespan; Particle swarm optimisation; Proportion; PSO; Wireless sensor network; WSN;

D O I：

10.1504/IJHPCN.2015.072788

中图分类号：

学科分类号：

摘要：

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 head's 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 network's lifespan. © 2015 Inderscience Enterprises Ltd.

引用

页码：370 / 380

页数：10

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