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 non-uniform 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. Firstly, 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. Secondly, 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 to existing protocols, NIHDC achieves at least 17.7% improvement in network lifetime while maintaining comparable tracking accuracy, and delays the emergence of coverage holes.

Multi-hop routing over low-power wide-area networks (LPWANs) has emerged as a promising technology for extending network coverage. However, existing protocols face high transmission disruption risks due to factors such as dynamic topology driven by stochastic events, dynamic link quality, and coverage holes induced by imbalanced energy consumption. To address this issue, we propose a failure risk-aware deep Q-network-based multi-hop routing (FRDR) protocol, aiming to reduce transmission disruption probability. First, we design a power regulation mechanism (PRM) that works in conjunction with pre-selection rules to optimize end-device node (EN) activations and candidate relay selection. Second, we introduce the concept of routing failure risk value (RFRV) to quantify the potential failure risk posed by each candidate next-hop EN, which correlates with its neighborhood state characteristics (i.e., the number of neighbors, the residual energy level, and link quality). Third, a deep Q-network (DQN)-based routing decision mechanism is proposed, where a multi-objective reward function incorporating RFRV, residual energy, distance to the gateway, and transmission hops is utilized to determine the optimal next-hop. Simulation results demonstrate that FRDR outperforms existing protocols in terms of packet delivery rate and network lifetime while maintaining comparable transmission delay.