An ever increasing number of applications can employ aerial unmanned vehicles, or so-called drones, to perform different sensing and possibly also actuation tasks from the air. In some cases, the data that is captured at a given point has to be processed before moving to the next one. Drones can exploit nearby edge servers to offload the computation instead of performing it locally. However, doing this in a naive way can be suboptimal if servers have limited computing resources and drones have limited energy resources. In this paper, we propose a protocol and resource reservation scheme for each drone and edge server to decide, in a dynamic and fully decentralized way, whether to offload the computation and respectively whether to accept such an offloading requests, with the objective to evenly reduce the drones' mission times. We evaluate our approach through extensive simulation experiments, showing that it can significantly reduce the mission times compared to a no-offloading scenario by up to 26.2%, while outperforming an offloading schedule that has been computed offline by up to 7.4% as well as a purely opportunistic approach by up to 23.9%.

翻译：越来越多的应用可以使用空中无人驾驶车辆（即所谓的无人机）从空中执行不同的感知任务，可能还包括执行任务。在某些情况下，在移动到下一个位置之前，需要先处理当前采集的数据。无人机可以利用附近的边缘服务器卸载计算任务，而非在本地执行。然而，如果服务器计算资源有限且无人机能量资源有限，以简单方式执行此操作可能并非最优。本文为每架无人机和边缘服务器提出了一种协议和资源预留方案，以动态且完全去中心化的方式决定是否卸载计算任务，以及是否接受此类卸载请求，目标在于均匀缩短无人机的任务执行时间。我们通过大量仿真实验评估了该方法，结果表明，与无卸载场景相比，该方法可将任务执行时间显著缩短最多达26.2%，同时相较于离线计算的卸载调度方案最多可提升7.4%，相较于纯机会主义方法最多可提升23.9%。