Intent‐Driven Diffusion‐Based Path for Mobile Data Collector in <scp>IoT</scp> ‐Enabled Dense <scp>WSNs</scp>

ABSTRACT Mobile data collection using controllable sinks is an effective approach for improving energy efficiency and data freshness in densely deployed wireless sensor networks (WSNs). However, existing path‐planning methods are often heuristic‐driven and lack the flexibility to adapt to high‐level operational objectives under dynamic network conditions. In this paper, we propose ID 2 P 2 , an intent‐driven, diffusion‐based path‐planning framework that jointly addresses rendezvous point selection and mobile data collector (MDC) tour construction in IoT‐enabled dense WSNs. High‐level intents, such as latency minimization, energy balancing, or coverage prioritization, are explicitly modeled and incorporated into a generative diffusion planning process that produces feasible and adaptive data collection trajectories. The proposed approach learns a trajectory prior that captures spatial node distribution and network characteristics, enabling the MDC to generate paths that align with specified intents while maintaining collision‐free and energy‐aware operation. Extensive simulations are conducted to evaluate the effectiveness of the proposed framework against conventional path‐planning baselines. The results demonstrate that ID 2 P 2 consistently outperforms representative baselines, achieving up to – reductions in tour completion time and travel overhead, approximately – improvements in data freshness, and %– gains in energy efficiency and packet delivery performance, while maintaining higher throughput and fairness as network density increases, thereby confirming its robustness and scalability for WSNs.