Research on Wireless Sensor Network Layout Based on Improved Particle Swarm Optimization Algorithm

This study proposes a method for wireless sensor network layout based on an improved particle swarm optimization algorithm. By introducing adaptive inertia weight and chaotic disturbance mechanisms, the algorithm effectively enhances global search capability and convergence speed. In a simulated environment of 100m × 100m, the algorithm achieves an average coverage rate of 95.6%, which is 2.8 percentage points higher than traditional methods, while reducing convergence time by 22.9%. Experimental results demonstrate that the improved algorithm exhibits excellent adaptability and stability in various scenarios, particularly in complex terrains and large-scale networks. The final layout scheme meets or exceeds expected targets in key metrics such as coverage, connectivity, and energy efficiency, with an estimated 40% extension in network lifespan. The research outcomes provide a robust tool for the efficient deployment of wireless sensor networks.