UAV Trajectory Optimization for Large-Scale and Low-Power Data Collection: An Attention-Reinforced Learning Scheme
文献类型：期刊论文
| 作者 | Zhu, Yuchen2,3; Yang, Bo1; Liu, Min2,3,4; Li, Zhongcheng2,3 |
| 刊名 | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
 |
| 出版日期 | 2024-04-01 |
| 卷号 | 23期号:4页码:3009-3024 |
| 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 |
| ISSN号 | 1536-1276 |
| DOI | 10.1109/TWC.2023.3304900 |
| 英文摘要 | 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. |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001201360000088 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/40020]  |
| 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.Northwest A&F Univ, Coll Informat Engn, Yangling 712100, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, 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. |
入库方式： OAI收割
来源：计算技术研究所
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