Novel WSN Coverage Optimization Strategy via Monarch Butterfly Algorithm and Particle Swarm Optimization

Abstract Wireless sensor networks (WSNs) show disadvantages of redundancy and relatively high network cost. Aiming at the problems of redundancy and increased network cost in the deployment of nodes in the wireless sensor network. Taking network coverage as the optimization goal, combined with the probability perception model, a new WSN coverage optimization method is proposed. With network coverage as the optimization goal, and a coverage optimization strategy for WSNs based on the improved monarch butterfly optimization algorithm is proposed. A mathematical model based on energy consumption, work efficiency, and coverage of the sensor network is established. To accelerate the convergence speed of the monarch butterfly algorithm, extend its search range, and prevent falling into local extreme values, the butterfly adjustment ratio is adjusted as per the number of iterations. A hybrid update rule is established based on the particle swarm optimization algorithm wherein the population is divided into three parts which are updated through migration, butterfly adjustment, and particle swarm update rules. A series of benchmarking functions are utilized to compare the proposed method with other WSN coverage optimization methods. The results show that the proposed algorithm effectively improves network coverage and node utilization while reducing network consumption.