Nodal Importance-Based Hierarchical Dynamic Clustering Scheme for Target Tracking in WSNs

Clustering for target tracking over wireless sensor networks (WSNs) is an important method to achieve a trade-off between network lifetime and tracking accuracy. Due to the stochastic and nonuniform distribution of nodes within the network, the untimely onset of coverage holes is exhibited in prevailing clustering schemes, thereby diminishing the overall network coverage performance. To address this issue, we propose the nodal importance-based hierarchical dynamic clustering (NIHDC) scheme. First, we introduce the concept of nodal importance rank to quantify the impact of regional states on individual nodes, which is correlated with the regional node density and energy levels. Second, by jointly considering energy factor and nodal importance rank, we propose an importance-ascending hierarchical scheduling algorithm to strike a balance between network lifetime and tracking accuracy, in which the Binary African Vulture Optimization Algorithm (BAVOA) is utilized to search for the optimal cluster. Simulation results demonstrate that, compared with existing protocols, NIHDC achieves at least 17.7% improvement in network lifetime while maintaining comparable tracking accuracy and delays the emergence of coverage holes.