Coverage Optimization Technology of Power Pipe Gallery Based on Improved Gray Wolf Algorithm

To address the problem of reduced communication quality in narrow underground power pipe gallery, where wireless sensor network coverage is affected by irregular shapes, obstacles, and electromagnetic interference, a power monitoring coverage sensing model is constructed based on the minimum access rate constraint, and an improved gray wolf coverage optimization algorithm is proposed by combining neuron mapping and differential evolution. Firstly, a uniform initial population is generated by neuron chaos mapping. Then, the nonlinear convergence factor is used to balance the global and local search ability. And finally, a differential evolution algorithm is introduced to mutate the gray wolf individuals. A comparative simulation analysis is made of various coverage optimization methods, and the results show that the proposed algorithm has robust search capabilities and it can significantly improve the network coverage performance in the narrow underground power pipe galleries, while effectively satisfying the communication needs of the monitored nodes.