Problem
Research questions and friction points this paper is trying to address.
UAV charging
Wireless Rechargeable Sensor Networks
Integrated Sensing and Communication
On-demand charging
Mobility uncertainty
ISAC-Enabled On-Demand UAV Charging for Wireless Rechargeable Sensor Networks
🤖 AI Summary
This work addresses service disruptions caused by node energy depletion in wirelessly rechargeable sensor networks by proposing a novel on-demand drone charging framework based on Integrated Sensing and Communication (ISAC). Coordinated by a base station, the framework dynamically prioritizes charging requests through a queue that incorporates both node urgency and service cost. It uniquely integrates ISAC-enabled real-time sensing to jointly optimize drone trajectory planning and charging scheduling in a bidirectionally coupled manner—marking the first application of ISAC to on-demand drone charging. By co-optimizing flight paths, charging sequences, and partial charging strategies, the approach significantly enhances energy utilization efficiency while reducing flight distance and charging latency. Extensive simulations demonstrate the superiority of the proposed method in terms of network longevity and quality of service.
📝 Abstract
Unmanned aerial vehicles (UAVs) equipped with wireless power transfer (WPT) extend the lifetime of wireless rechargeable sensor networks (WRSNs) by delivering energy on demand. This article presents an integrated sensing and communication (ISAC)-enabled on-demand UAV charging framework coordinated by a central base station. A prioritized charging queue captures node urgency and service cost through residual energy, traffic load, estimated UAV travel time, and flight-direction alignment. This bidirectional coupling ensures that scheduling decisions shape the UAV trajectory, while updated mobility estimates from ISAC dynamically reorder the queue. ISAC-assisted estimation of UAV distance, speed, and position updates travel-time predictions under mobility uncertainty. A time-allocated partial charging policy distributes limited hover time across queued nodes according to criticality. Simulations show gains in energy usage efficiency, travel distance, and charging delay compared with representative baselines. We discuss deployment considerations, including computational overhead, scalability, and parameter selection, to aid practitioners evaluating the framework for IoT scenarios.
Innovation
Methods, ideas, or system contributions that make the work stand out.
ISAC
UAV charging
wireless power transfer
priority scheduling
mobility-aware trajectory planning
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
