A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization
College of Communication Engineering, Jilin University, Changchun 130012, China
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Sensors 2019, 19(12), 2735; https://doi.org/10.3390/s19122735
Received: 10 May 2019 / Revised: 5 June 2019 / Accepted: 11 June 2019 / Published: 18 June 2019
(This article belongs to the Section Sensor Networks)
The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embedded Grey Wolf Optimizer, Cuckoo Search algorithm, and Chaotic Particle Swarm Optimization. The VFLGWO algorithm has good adaptability with respect to changes of the number of sensor nodes and the size of the monitoring area.
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Keywords:
wireless sensor network; Lévy-embedded Grey Wolf Optimization algorithm; Virtual Force algorithm; coverage optimization
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MDPI and ACS Style
Wang, S.; Yang, X.; Wang, X.; Qian, Z. A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization. Sensors 2019, 19, 2735. https://doi.org/10.3390/s19122735
AMA Style
Wang S, Yang X, Wang X, Qian Z. A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization. Sensors. 2019; 19(12):2735. https://doi.org/10.3390/s19122735
Chicago/Turabian StyleWang, Shipeng; Yang, Xiaoping; Wang, Xingqiao; Qian, Zhihong. 2019. "A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization" Sensors 19, no. 12: 2735. https://doi.org/10.3390/s19122735
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