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A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

Jin Wang1,2, Chunwei Ju2, Yu Gao2, Arun Kumar Sangaiah3, Gwang-jun Kim4,*
School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha, 410114, China.
School of Information Engineering, Yangzhou University, Yangzhou, 225009, China.
School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India.
Department of Computer Engineering, Chonnam National University, Gwangju, 61186, Korea.
* Corresponding Author: Gwang-jun Kim. Email: .

Computers, Materials & Continua 2018, 56(3), 433-446. https://doi.org/ 10.3970/cmc.2018.04132

Abstract

Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the whole network is partitioned into grids, and we calculate each grid’s coverage rate and energy consumption. Finally, each sensor nodes’ sensing radius is adjusted according to the coverage rate and energy consumption of each grid. Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption

Keywords

Particle swarm optimization, Coverage control, Energy efficiency, Wireless sensor networks

Cite This Article

J. . Wang, C. . Ju, Y. . Gao, A. K. . Sangaiah and G. . Kim, "A pso based energy efficient coverage control algorithm for wireless sensor networks," Computers, Materials & Continua, vol. 56, no.3, pp. 433–446, 2018.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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