Elite Opposition-Based Bare Bones Mayfly Algorithm for Optimization Wireless Sensor Networks Coverage Problem

Wireless sensor networks (WSNs) are composed of sensor nodes with sensing, computing and wireless communication abilities. All sensor nodes have the task of monitoring environmental conditions, collecting and transmitting data. In the field of WSNs, maximizing the coverage of the target region is a key optimization problem. Mayfly algorithm (MA) is a metaheuristic algorithm, which has been successfully implemented to solve various practical issues. This paper presents an elite opposition-based bare bones mayfly algorithm (EOBBMA), which introduces Gaussian distribution and Lévy flight, ameliorates the shortage of too many initial parameters of MA, and introduces an elite opposition-based learning (EOBL) strategy to enhance the exploration ability of the algorithm. Finally, we use EOBBMA to relocate the mobile sensors after the initial deployment, aiming at maximizing the coverage area, minimizing the redundant area and minimizing the moving distance. EOBBMA and seven other algorithms are tested in eight scales. The experimental results reveal that the coverage rate of the sensor deployment scheme obtained by EOBBMA is more than 85%, and the highest coverage is 99.6%. Considering all aspects of performance, compared with other algorithms, EOBBMA has significant superiority for WSNs coverage problem.