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

Wireless sensor networks (WSNs) have issues with duplication and comparatively expensive network installation costs. A unique approach to maximizing WSN coverage is put forth to overcome these problems by making the coverage the primary goal and using a probability perception model. To enhance the WSN’s coverage, the method uses a better monarch butterfly optimization algorithm. A mathematical model based on energy consumption, operational performance, and coverage is offered to assure effective sensor placement. To hasten convergence, enlarge the search space, and avoid local extremes, the butterfly adjustment ratio is appropriately set after the iteration number. Using a hybrid update mechanism built on the particle swarm optimization technique, the population is split into three groups: migration, butterfly adjustment, and particle swarm update. To compare the performance of the proposed method with current WSN coverage optimization techniques, a number of benchmarking functions are employed. The findings demonstrate that the recommended approach effectively reduces network expenses while enhancing network coverage and node use.