Optimizing the wireless sensor network coverage algorithm of slime mold

This article introduces a wireless sensor network coverage algorithm based on slime mold optimization, aimed at enhancing coverage effectiveness and expanding the coverage range of sensor nodes in the target area. Firstly, the slime mold population is initialized based on the positions of wireless sensor nodes, with node coverage serving as the fitness function, and a network coverage system model is established. Subsequently, adaptive t-distribution mutation is introduced, along with an oscillating search mechanism using the sine-cosine algorithm and Cauchy cumulative distribution operator, to enhance the position updating mechanism of the slime mold algorithm. Finally, by introducing a random differential mutation mechanism and a greedy comparison mechanism, the slime mold population with the best fitness value is selected as the optimal solution for the distribution of sensor nodes. Simulation results demonstrate that the proposed algorithm achieves a coverage rate of 89.1%, which is respectively 11.6%, 9%, 6%, and 5.6% higher compared to WOA, EO, SMA, and ESMA algorithms.