Wireless Sensor Network Coverage Optimization Based on Improved PSO

Abstract: To improve the coverage of Wireless Sensor Networks (WSN), a Modified Particle Swarm Optimization (MPSO) algorithm based on mutual learning ability and dynamic learning factors is proposed. A Latin Hypercube Sampling (LHS) sequence is introduced to initialize the population, increasing the diversity of the population and laying the foundation for subsequent optimization; a mutual learning method is introduced, where particles enhance their learning ability by randomly selecting target particles, improving local optimization performance; a dynamic learning factor strategy is utilized, changing the learning ability of particles, accelerating the convergence speed of the algorithm and enhancing global optimization capability. Simulation results show that, without changing the original algorithm’s complexity, the improved PSO algorithm can achieve better optimization results with fewer resources compared to the basic PSO algorithm and other comparative algorithms, effectively solving the network coverage blind spots and redundancy issues, thereby improving network coverage.

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