A Continuum Approach for Collaborative Task Processing in UAV MEC Networks
A Continuum Approach for Collaborative Task Processing in UAV MEC Networks
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DOI：
10.1109/cloud55607.2022.00046
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Lorson Blair;Carlos A. Varela;S. Patterson
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文献类型：
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作者：
Lorson Blair;Carlos A. Varela;S. Patterson
无人机（UAV）正在成为一个可行的平台，用于各种各样的应用，包括灾害响应，搜索和救援，以及安全监控。这些传感无人机的电池和计算能力有限，因此必须卸载它们的数据，以便处理这些数据以提供可操作的情报。我们考虑由有限数量的高资源无人机组成的计算平台，这些无人机充当移动的边缘计算（MEC）服务器来处理本地工作负载。我们提出了一种新的分布式解决方案的协同处理问题，自适应定位MEC无人机响应不断变化的工作量，既从传感无人机的移动性和任务生成。我们的解决方案包括两个关键的构建块：（1）一个有效的工作量估计过程，无人机估计任务领域的一个连续的近似任务的数量在空域中的每个位置，和（2）分布式优化方法，无人机分区的任务领域，以最大限度地提高系统的吞吐量。我们评估我们提出的解决方案，使用现实的模型，无人机的监视移动性，并表明我们的方法实现了高达28%的吞吐量提高了非自适应基线方法。
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.