Minimum distance clustering algorithm based on an improved differential evolution

Authors:
Xiangyuan Yin;Zhihao Ling;Liping Guan;Feng Liang
Affiliations:
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China/ Faculty of Electronic and Information Engineering, Zhejiang Wanli University ...;School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;Zhejiang Wanli University, Ningbo 315100, China;Zhejiang Wanli University, Ningbo 315100, China
Venue:
International Journal of Sensor Networks
Year:
2014

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Abstract

The goals of wireless sensor networks WSNs are to sense and collect data and to transmit the information to a sink. Because the sensor nodes are typically battery powered, the main challenges in WSNs are to optimise the energy consumption and to prolong the network lifetime. This paper proposes a centralised clustering algorithm termed the minimum distance clustering algorithm that is based on an improved differential evolution MD-IDE. The new algorithm combines the advantages of simulated annealing and differential evolution to determine the cluster heads CHs for minimising the communication distance of the WSN. Many simulation results demonstrate that the performance of MD-IDE outperforms other well-known protocols, including the low-energy adaptive clustering hierarchy LEACH and LEACH-C algorithms, in the aspects of reducing the communication distance of the WSN for reducing energy consumption.