Node Collaborative Strategy for 3D Coverage Based on Hopping Adaptive Grey Wolf Optimizer in Wireless Sensor Network

Wireless sensor networks (WSNs) represent an emerging technology, among which coverage optimization remains a fundamental challenge. In specific application scenarios such as intelligent urban management, three-dimensional (3D) coverage models better reflect real-world requirements and thus hold greater research significance. To maximize the coverage performance of 3DWSNs, this study proposes a Three-Dimensional Confident Information Coverage (3DCIC) model based on the concept of multi-node cooperative information reconstruction, effectively extending the perceptual domain of sensor nodes. Furthermore, by incorporating adaptive dimension learning and opposition-based learning metchanisms into the wolf pack update strategy, we have developed the Hopping Adaptive Grey Wolf Optimizer (HAGWO) based on the GWO to optimize node deployment. Experimental results demonstrate the superior performance of the 3DCIC model, achieving coverage ranges 2.78 times, 4.41 times, and 4.00 times greater than those of conventional binary spherical models under regular tetrahedral, hexahedral, and octahedral node deployments, respectively. The proposed scheduling algorithm proves highly effective in both classical test functions and three-dimensional coverage problems.