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）在智慧农田上空悬停，能够支持处理能力与功率受限的物联网（IoT）设备高效、经济地完成其时延敏感型任务。本文提出一种基于图神经网络-强化学习的解决方案，用于优化这些物联网设备向无人机的任务卸载策略。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了任务执行时长。此外，所提方案对网络拓扑变化具有更强的鲁棒性，并能适应无人机失效等极端情况。