无人机MEC网络协同任务处理的连续体方法

Q1 Computer Science
Lorson Blair, Carlos A. Varela, S. Patterson
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引用次数: 1

摘要

无人驾驶飞行器(uav)正在成为各种应用中传感和估计的可行平台,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,因此必须卸载数据,以便处理数据以提供可操作的情报。我们考虑了一个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来解决协同处理问题,该解决方案可以自适应地定位MEC无人机,以响应由感知无人机的机动性和任务生成引起的工作量变化。我们的解决方案由两个关键构建块组成:(1)高效的工作量估计过程,无人机通过该过程估计任务域-连续逼近空域中每个位置要处理的任务数量;(2)分布式优化方法,无人机通过该方法划分任务域以最大化系统吞吐量。我们使用监视无人机机动性的现实模型评估了我们提出的解决方案,并表明我们的方法比非自适应基线方法实现了高达28%的吞吐量改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Continuum Approach for Collaborative Task Processing in UAV MEC Networks
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.
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来源期刊
IEEE Cloud Computing
IEEE Cloud Computing Computer Science-Computer Networks and Communications
CiteScore
11.20
自引率
0.00%
发文量
0
期刊介绍: Cessation. IEEE Cloud Computing is committed to the timely publication of peer-reviewed articles that provide innovative research ideas, applications results, and case studies in all areas of cloud computing. Topics relating to novel theory, algorithms, performance analyses and applications of techniques are covered. More specifically: Cloud software, Cloud security, Trade-offs between privacy and utility of cloud, Cloud in the business environment, Cloud economics, Cloud governance, Migrating to the cloud, Cloud standards, Development tools, Backup and recovery, Interoperability, Applications management, Data analytics, Communications protocols, Mobile cloud, Private clouds, Liability issues for data loss on clouds, Data integration, Big data, Cloud education, Cloud skill sets, Cloud energy consumption, The architecture of cloud computing, Applications in commerce, education, and industry, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Business Process as a Service (BPaaS)
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