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High-efficiency UAV-assisted Data Collection Method Leveraging Reinforcement Learning
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Jialin ZHU1, Penghao ZHANG2, Nanxi LI1, Zheng JIANG1, Jianchi ZHU1
Radio Communications Technology | 2025, 51(5) : 940 - 950
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Radio Communications Technology | 2025, 51(5): 940-950
Special Topic: 6G and IoT Technologies
High-efficiency UAV-assisted Data Collection Method Leveraging Reinforcement Learning
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Jialin ZHU1, Penghao ZHANG2, Nanxi LI1, Zheng JIANG1, Jianchi ZHU1
Affiliations
  • 1.China Telecom Research Institute, Beijing 102209, China
  • 2.School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Published: 2025-09-18 doi: 10.3969/j.issn.1003-3114.2025.05.007
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The Internet of Things (IoT), as one core area of 6G development, plays a crucial role in driving network architecture changes and supporting core application scenarios. However, IoT systems suffer from energy imbalances and short network lifecycles, which severely restrict the improvement of data collection efficiency. With the rise of Unmanned Aerial Vehicle (UAV) technology, its high maneuverability can effectively construct Line of Sight (LOS) communication links, thereby improving communication speed. This has great application value in data collection of IoT systems and can solve the problem of low data collection efficiency caused by the short lifecycle of IoT networks. To this end, UAVs are used to collect data from ground IoT devices and build a data collection and transmission link for air-to-ground collaboration. An intelligent data collection method based on Deep Reinforcement Learning (DRL) is proposed. In addition, a predictive neural network is designed to further improve data collection efficiency by predicting network data at the Base Station (BS) side, thereby achieving the goal of reducing IoT device energy consumption and extending network lifespan. Simulation results show that the proposed data collection algorithm has good performance advantages in terms of device energy consumption and energy balance, and is superior to traditional data collection algorithms. At the same time, the proposed data collection network architecture can extend the network lifespan by 1.2 times when the predicted data accounts for 12.5%. In addition, simulations have shown that the designed predictive neural network outperforms other compared networks in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE) metrics.

6G  /  IoT  /  data collection  /  UAV  /  reinforcement learning
Jialin ZHU, Penghao ZHANG, Nanxi LI, Zheng JIANG, Jianchi ZHU. High-efficiency UAV-assisted Data Collection Method Leveraging Reinforcement Learning[J]. Radio Communications Technology, 2025 , 51 (5) : 940 -950 . DOI: 10.3969/j.issn.1003-3114.2025.05.007
Year 2025 volume 51 Issue 5
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doi: 10.3969/j.issn.1003-3114.2025.05.007
  • Receive Date:2025-05-27
  • Online Date:2026-04-17
  • Published:2025-09-18
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  • Received:2025-05-27
Affiliations
    1.China Telecom Research Institute, Beijing 102209, China
    2.School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
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小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
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