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Energy-Efficient UAV-Aided Target Tracking Systems Based on Edge Computing
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AI
DOI：10.1109/JIOT.2021.3091216.png)
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摘要
En 中文
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.
Keyword:
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)
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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