期刊
IEEE INTERNET OF THINGS JOURNAL
卷 9, 期 3, 页码 2207-2214出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3091216
关键词
Target tracking; Task analysis; Energy consumption; Unmanned aerial vehicles; Edge computing; Trajectory; Streaming media; Edge computing; energy consumption; mobile target; tracking; unmanned aerial vehicle (UAV)
资金
- National Natural Science Foundation of China [61772553]
- Central South University [2020zzts612, GCX20190884Y]
This article develops an energy-efficient UAV-aided target tracking system by offloading video processing tasks from UAV to edge nodes. The proposed algorithm achieves higher energy efficiency and lower latency in UAV-aided target tracking compared to existing methods.
Unmanned-aerial-vehicle (UAV)-aided target tracking has been applied in many important practical scenarios such as target vehicle tracking missions. However, the limited computation capability of UAVs can hardly support computation-intensive tasks, like the target tracking with real-time video processing. Inspired by the strong computation capabilities of edge computing servers nowadays, this article develops an energy-efficient UAV-aided target tracking system, where the video processing tasks can be offloaded from a UAV to the edge nodes (ENs) along its flight trajectory. To select appropriate offloading ENs for efficient task processing and energy saving, we formulate a cost minimization problem by jointly optimizing the task execution time and the offloading energy consumption. To devise a practical offloading strategy, we propose an energy-efficient UAV's task distribution (EUTD) algorithm by jointly taking the different computation capabilities among ENs, time and energy requirements for different tasks, and fast-changing wireless channel conditions into account. Extensive experimental results demonstrate that our proposed algorithm can achieve significantly higher energy efficiency and lower latency in UAV-aided target tracking as compared with existing methods.
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