用强化学习优化无人机边缘计算，提升灾时设备续航与通信可靠性。
Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices
- 通过强化学习动态调度无人机，应对电力与通信中断风险。
- 模拟显示该方法可显著延长关键边缘设备运行时间。
- 适合应急响应、智慧城市建设等场景的系统设计者参考。
边缘感知与计算正迅速成为智能基础设施的一部分，尤其在灾害或紧急情况下依赖度越来越高。此类场景中，因电网故障或基站损毁（如洪水、野火）导致供电与通信系统失效的风险极高。为此，本文提出利用无人飞行器（UAV）作为移动边缘计算节点，为边缘设备提供计算卸载以节省电池电量，并作为中继节点保障通信。研究进一步考虑了无人机自身的电源与连接限制，在多种电力与通信故障情景下应用强化学习，识别出最可能失效的设备，为维护人员提供优先级指导。通过模拟乡村小镇和城市中心区域的疏散场景，验证了该方法在延长关键边缘设备寿命方面的有效性。
原文摘要 · Abstract (English)
Edge sensing and computing is rapidly becoming part of intelligent infrastructure architecture leading to operational reliance on such systems in disaster or emergency situations. In such scenarios there is a high chance of power supply failure due to power grid issues, and communication system issues due to base stations losing power or being damaged by the elements, e.g., flooding, wildfires etc. Mobile edge computing in the form of unmanned aerial vehicles (UAVs) has been proposed to provide computation offloading from these devices to conserve their battery, while the use of UAVs as relay network nodes has also been investigated previously. This paper considers the use of UAVs with further constraints on power and connectivity to prolong the life of the network while also ensuring that the data is received from the edge nodes in a timely manner. Reinforcement learning is used to investigate numerous scenarios of various levels of power and communication failure. This approach is able to identify the device most likely to fail in a given scenario, thus providing priority guidance for maintenance personnel. The evacuations of a rural town and urban downtown area are also simulated to demonstrate the effectiveness of the approach at extending the life of the most critical edge devices.
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