Year: 2026
Type: preprint
Abstract: Mobile data collection using controllable sinks is an effective approach to improve 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 ID2P2, a intent-driven diffusion-based path planning framework for 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 ID2P2 consistently outperforms representative baselines, achieving up to 25-30% reduction in tour completion time and travel overhead, approximately 10-30% improvement in data freshness, and 15-30% gains in energy efficiency and packet delivery performance, while maintaining higher throughput and fairness as network density increases, confirming its robustness and scalability for WSNs.
Source: arXiv (Cornell University)
Authors: Boda, Uma Mahesh, Nuka, Mallikharjuna Rao
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7129357675
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