A Deployment Optimization Algorithm for WSNs based on Adaptive Virtual Force Disturbance Sparrow Search
Xiaoqiang Zhao, Min Liu, Yanpeng Cui, Yindi Yao · 2021 4th International Conference on Artificial Intelligence and Pattern Recognition · 2021
When environmental monitoring and target tracking are carried out in harsh and unattended environment, the deployment of nodes in wireless sensor networks affects the monitoring effect and deployment cost of the network. In order to effectively improve network coverage and utilization of network resource, and achieve the best coverage effect by deploying the least number of network nodes, a coverage optimization strategy for wireless sensor networks based on adaptive virtual force disturbance sparrow search algorithm is proposed. Firstly, the adaptive virtual force algorithm is designed for different scale deployment, and the sparrow search algorithm is disturbed by the adaptive virtual force algorithm, which accelerates the convergence speed of the algorithm. Secondly, by analyzing the scrounger characteristics of the sparrow search algorithm, an elite selection strategy is designed to improve the sparrow search algorithm, which overcomes the disadvantage that the algorithm is easy to fall into local optimum in the later iteration stage and improves the global convergence performance of the algorithm. The simulation results indicate that compared with the coverage efficient virtual force algorithm, the sparrow search algorithm and the virtual force-directed particle swarm optimization, this final coverage rate of this algorithm increased by 8.6%, 4.6%, 4.1%, and coverage efficiency improved by 6.3%, 3.3%, 2.9%, respectively. In addition, the algorithm has obvious performance advantages in terms of moving distance of nodes.