Abstract
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.
Highlights
Wireless sensor networks (WSNs) were originally designed for military applications, currently WSNs are widely used in civilian applications, including vehicle tracking, forest monitoring, seismic observation, building monitoring, and water resource monitoring [1,2]
Wireless sensor nodes are typically placed randomly in the monitored area, which results in a non-uniform distribution of sensor nodes and a low coverage rate of the monitored area
This paper proposes a wireless sensor network coverage optimization algorithm, the Virtual
Summary
Wireless sensor networks (WSNs) were originally designed for military applications, currently WSNs are widely used in civilian applications, including vehicle tracking, forest monitoring, seismic observation, building monitoring, and water resource monitoring [1,2].The coverage rate is an important measure of WSN performance. It is of great significance to improve the coverage rate of WSNs in the monitored area by adjusting the position of sensor nodes. Algorithms and solutions for a distributed problem inspired by the collective behavior of social insects and animal groups are part of swarm intelligence research. This type of algorithm is called a swarm intelligence optimization algorithm. In 2009, Yang and Deb of Cambridge University proposed a new biological heuristic algorithm, namely the Cuckoo Search algorithm [5] This algorithm is widely used because of its simple structure, few control parameters, and strong search capability
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