Having unmanned aerial vehicles (UAVs) with edge computing capability hover over smart farmlands supports Internet of Things (IoT) devices with low processing capacity and power to accomplish their deadline-sensitive tasks efficiently and economically. In this work, we propose a graph neural network-based reinforcement learning solution to optimize the task offloading from these IoT devices to the UAVs. We conduct evaluations to show that our approach reduces task deadline violations while also increasing the mission time of the UAVs by optimizing their battery usage. Moreover, the proposed solution has increased robustness to network topology changes and is able to adapt to extreme cases, such as the failure of a UAV.

翻译：具备边缘计算能力的无人机（UAV）悬停于智慧农田上方，能够为处理能力与功耗受限的物联网（IoT）设备提供支持，帮助其高效、经济地完成对截止时间敏感的任务。本文提出一种基于图神经网络的强化学习解决方案，用于优化物联网设备向无人机的任务卸载过程。实验评估表明，该方法在降低任务截止时间违反率的同时，通过优化无人机电池使用效率延长其任务执行时间。此外，所提方案对网络拓扑变化具有更强的鲁棒性，并能适应极端情况（如单架无人机故障）。