Multi-strategy enhanced grey wolf algorithm for obstacle-aware WSNs coverage optimization

Moving sensor nodes can mitigate the coverage problem of random deployment in wireless sensor networks. However, the movement of nodes affects the lifetime and integrity of the network. Therefore, both energy saving and efficient coverage are crucial factors. In this paper, we propose an energy-efficient coverage optimization technique with the help of the multi-Strategy grey wolf optimization (MSGWO) algorithm. This method can reduce energy consumption and improve coverage area by mixing higher-order multinomial sensing models and a sort-driven hybrid opposition-based learning. In addition, node movement and boundary strategies are proposed to help nodes jump out of obstacles when facing obstacle-aware deployments. The MSGWO is validated and compared on several classical test functions, and the results show that the MSGWO performs well. The MSGWO algorithm is applied to optimize the WSN coverage on different obstacle scenarios, the experimental results show that the algorithm helps to increase the network coverage from 84 % to 97.86 %, extends the network lifecycle by 50 %, reduces the cost of node deployment, and the network has good connectivity and scalability.

Automated summary

In this paper, an energy-efficient coverage optimization technique using the multi-Strategy grey wolf optimization (MSGWO) algorithm is proposed. The method combines higher-order multinomial sensing models and a sort-driven hybrid opposition-based learning to reduce energy consumption and improve coverage area. Node movement and boundary strategies are introduced to overcome obstacles. Experimental results show that the algorithm significantly increases network coverage, extends network lifecycle, reduces deployment cost, and ensures good connectivity and scalability.