UAV Trajectory Optimization for Large-Scale and Low-Power Data Collection: An Attention-Reinforced Learning Scheme

doi:10.1109/TWC.2023.3304900

	UAV Trajectory Optimization for Large-Scale and Low-Power Data Collection: An Attention-Reinforced Learning Scheme
	Zhu, Yuchen 1,2; Yang, Bo 3; Liu, Min 1,2,4; Li, Zhongcheng 1,2
	2024-04-01
发表期刊	IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (IF:5.888[JCR-2017],5.855[5-Year])
ISSN	1536-1276
卷号	23 期号:4 页码:3009-3024
摘要	Unmanned Aerial Vehicles (UAVs) exhibit great advantages in data collection from ground sensors in vast tracts of fields. Due to their limited power supply, most works assume that the UAV simply traverses each sensor's fixed transmission range to collect data, thereby shortening the flight path. However, they neglect the quality of collected data, which may deteriorate dramatically as the transmission distance increases. In this paper, by leveraging the physical-layer protocol - LoRa, we propose a Packet Reception Ratio (PRR)-based probabilistic coverage model to evaluate the quality of data transmission, which directly determines the data acquisition efficiency. On this basis, to minimize the energy consumption of UAV and sensors while ensuring high-quality data acquisition, we formulate the UAV trajectory planning as a joint Energy Consumption and data Acquisition Efficiency (ECAE) optimization problem. To tackle the ECAE problem, we propose a Deep Reinforcement Learning (DRL)-based two-stage scheme. First, an attention-based encoder-decoder model is trained to generate an initial trajectory. Then an intuitive optimization algorithm is devised to further explore the optimal trajectory. Evaluation results show that our scheme can reduce the total energy cost of UAV and sensors by 27.1% as compared to the best baseline's policy while maintaining a promising PRR.
关键词	Autonomous aerial vehicles Sensors Data collection Energy consumption Wireless communication Optimization Data models Unmanned aerial vehicle trajectory optimization data collection attention model deep reinforcement learning LoRa
DOI	10.1109/TWC.2023.3304900
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001201360000088
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40021
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Min
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 3.Northwest A&F Univ, Coll Informat Engn, Yangling 712100, Peoples R China 4.Zhongguancun Lab, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Yuchen,Yang, Bo,Liu, Min,et al. UAV Trajectory Optimization for Large-Scale and Low-Power Data Collection: An Attention-Reinforced Learning Scheme[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,2024,23(4):3009-3024.
APA	Zhu, Yuchen,Yang, Bo,Liu, Min,&Li, Zhongcheng.(2024).UAV Trajectory Optimization for Large-Scale and Low-Power Data Collection: An Attention-Reinforced Learning Scheme.IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,23(4),3009-3024.
MLA	Zhu, Yuchen,et al."UAV Trajectory Optimization for Large-Scale and Low-Power Data Collection: An Attention-Reinforced Learning Scheme".IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 23.4(2024):3009-3024.