Enhanced Network Performance in WSNs through the Improved PSO-MBO Coverage Algorithm

Wireless sensor networks (WSNs) face significant challenges, including redundancy and high operational costs, which necessitate the development of efficient coverage optimization methods. This research introduces the Improved Particle Swarm Optimization - Monarch Butterfly Optimization (PSO-MBO) Coverage algorithm, which aims to enhance network performance by maximizing coverage and optimizing resource utilization. The originality of this method lies in its integration of monarch butterfly optimization and particle swarm optimization, coupled with a probability perception model to ensure robust coverage across the network. Key considerations include minimizing energy consumption, improving efficiency, and maximizing sensor deployment effectiveness. Quantitative results reveal that the Improved PSO-MBO algorithm outperforms traditional methods, achieving maximum network coverage of 0.97 at 800 iterations and maintaining low energy consumption of 0.02 J across varying node counts. In contrast, PSO reached only 0.38 J at 50 nodes, underscoring the efficiency of the proposed method. The dynamic adjustment of the butterfly ratio and a hybrid update mechanism further speed up the convergence and enhances search efficiency. Comparative evaluations demonstrate that improved PSO-MBO significantly improves network coverage, node utilization, and overall energy efficiency, making it a more choice for optimizing WSN performance.