用于无线可充电传感器网络的基于可集成感知与通信的按需无人机充电
ISAC-Enabled On-Demand UAV Charging for Wireless Rechargeable Sensor Networks
AI总结：
研究无线可充电传感器网络中无人机按需充电问题，提出基于ISAC的框架，通过优先级充电队列、双向耦合及ISAC辅助估计等方法，实现高效充电调度，仿真显示相比基线有优势，并讨论了部署考量。
AI中文摘要：
配备无线电力传输（WPT）的无人机通过按需输送能量来延长无线可充电传感器网络（WRSN）的寿命。本文提出了一种由中央基站协调的基于可集成感知与通信（ISAC）的按需无人机充电框架。一个优先级充电队列通过剩余能量、流量负载、估计的无人机飞行时间和飞行方向对齐来捕捉节点紧迫性和服务成本。这种双向耦合确保调度决策塑造无人机轨迹，而来自ISAC的更新后的移动性估计会动态地重新排列队列。ISAC辅助的无人机距离、速度和位置估计在移动性不确定的情况下更新飞行时间预测。一种时间分配的部分充电策略根据关键性在排队节点之间分配有限的悬停时间。仿真表明，与代表性基线相比，在能源使用效率、飞行距离和充电延迟方面有提升。我们讨论了部署考虑因素，包括计算开销、可扩展性和参数选择，以帮助从业者评估该框架在物联网场景中的适用性。
英文摘要：
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.
