Title: A global-to-local searching-based binary particle swarm optimisation algorithm and its applications in WSN coverage optimisation

Authors: Kangshun Li; Ying Feng; Dunmin Chen; Shanni Li

Addresses: School of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China; Lab of Data Analysis and Processing of Guangdong Province, Sun Yat-sen University, Guangzhou, 510006, China ' School of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China ' School of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China ' Deyi Information Technology Co., Ltd., 33 Huilian Road, Qingpu District, Shanghai, 201707, China

Abstract: Heuristic search algorithms have been applied to the coverage optimisation problem of WSNs in recent years because of their strong search ability and fast convergence speed. This paper proposes an optimisation algorithm for a WSN based on improved binary particle swarm optimisation (PSO). The position updating formula based on the sigmoid transformation function is adjusted, and a global-to-local search strategy is used in the global-to-local searching-based binary particle swarm optimisation algorithm (GSBPSO). Furthermore, to apply GSBPSO to the optimisation of WSNs, a small probability mutation replacement strategy is proposed to replace individuals who do not meet the coverage requirements in the search process. In addition, the fitness function is improved so that the network density can be adjusted by modifying the parameters in the improved fitness function. Experiments show that the proposed algorithm in this paper is effective.

Keywords: WSNs; wireless sensor networks; BPSO; binary particle swarm optimisation; coverage optimisation; minimum connected coverage set; constrained problem.

DOI: 10.1504/IJSNET.2020.106599

International Journal of Sensor Networks, 2020 Vol.32 No.4, pp.197 - 208

Received: 09 Nov 2019
Accepted: 09 Nov 2019
Published online: 15 Apr 2020 *

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