Unmanned aerial vehicles (UAVs) are becoming a viable platform for sensing and estimation in a wide variety of applications including disaster response, search and rescue, and security monitoring. These sensing UAVs have limited battery and computational capabilities, and thus must offload their data so it can be processed to provide actionable intelligence. We consider a compute platform consisting of a limited number of highly-resourced UAVs that act as mobile edge computing (MEC) servers to process the workload on premises. We propose a novel distributed solution to the collaborative processing problem that adaptively positions the MEC UAVs in response to the changing workload that arises both from the sensing UAVs' mobility and the task generation. Our solution consists of two key building blocks: (1) an efficient workload estimation process by which the UAVs estimate the task field - a continuous approximation of the number of tasks to be processed at each location in the airspace, and (2) a distributed optimization method by which the UAVs partition the task field so as to maximize the system throughput. We evaluate our proposed solution using realistic models of surveillance UAV mobility and show that our method achieves up to 28% improvement in throughput over a non-adaptive baseline approach.
翻译:无人驾驶航空飞行器(无人驾驶航空飞行器)正在成为各种应用,包括救灾、搜索和救援以及安全监测在内的各种应用中进行感测和估算的可行平台。这些遥感无人驾驶航空飞行器的电池和计算能力有限,因此必须卸载数据,以便进行处理以提供可操作的情报。我们考虑一个计算平台,由数量有限的高度资源无人驾驶航空飞行器组成,作为移动边缘计算(MEC)服务器,处理房舍工作量。我们建议对协作处理问题提出新的分布式解决办法,根据遥感无人驾驶航空机动性和任务生成带来的不断变化的工作量,调整MEC无人驾驶航空飞行器的位置。我们的解决办法包括两个关键构件:(1) 高效的工作量估计过程,无人驾驶航空飞行器据以估计任务领域——连续近似空空域每个地点所要处理的任务数量,(2) 分散优化方法,无人驾驶航空飞行器对任务领域进行分配,以便最大限度地实现系统吞吐量。我们利用现实的监视无人驾驶航空机动飞行器流动模型来评估我们提出的解决办法,并表明我们的方法通过非适应基线实现28%的改进。