Energy-Efficient UAV-Aided Target Tracking Systems Based on Edge Computing

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Department of Electrical and Computer Engineering


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 from edge computing servers nowadays, this paper 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 hiratical computation capabilities among ENs, time and energy requirements for different task, 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.

Publication Title

IEEE Internet of Things Journal