Search

Ensuring the efficient operation of urban sensing systems under emergency conditions represents a current and significant challenge. To address the typical problem of low network coverage due to random deployment of wireless sensor network (WSN) nodes in urban sensing systems, a novel coverage optimization scheme with an enhanced marine predator algorithm (EMPA) is proposed. For a given target network monitoring area, a joint node sensing model based on Boolean sensing is constructed with the environmental information of the monitoring area as input, and then the model is optimized using EMPA for several iterations to obtain the optimized maximum network coverage and its corresponding optimal node location deployment scheme. Based on the standard MPA, first, the Tent chaotic map is introduced to initialize the population to maintain the population diversity of the algorithm; second, the adaptive jump step factor is introduced in the jump phase to improve the ability of the algorithm to jump out of the local optimum; finally, the double-subgroup location update strategy is introduced before the end of each iteration of the algorithm, which can better balance the local and global search and further enhance the algorithm’s optimal search capability. The algorithm is compared with four unimodal and multimodal benchmark test functions and applied to three network coverage optimization scenarios in urban sensing systems. The results show that the EMPA algorithm can efficiently optimize the network nodes’ uniform distribution, enhance average network coverage, and save a certain level of node deployment costs for urban sensing systems.