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Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach

Author

Listed:
  • Qianqian Wu

    (Beijing Key Laboratory of Transportation Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Qiang Liu

    (Beijing Key Laboratory of Transportation Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Zefan Wu

    (Beijing Key Laboratory of Transportation Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Jiye Zhang

    (School of Information Communication and Engineering, Communication University of China, Beijing 100024, China)

Abstract

In the field of ocean data monitoring, collaborative control and path planning of unmanned aerial vehicles (UAVs) are essential for improving data collection efficiency and quality. In this study, we focus on how to utilize multiple UAVs to efficiently cover the target area in ocean data monitoring tasks. First, we propose a multiagent deep reinforcement learning (DRL)-based path-planning method for multiple UAVs to perform efficient coverage tasks in a target area in the field of ocean data monitoring. Additionally, the traditional Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) algorithm only considers the current state of the agents, leading to poor performance in path planning. To address this issue, we introduce an improved MATD3 algorithm with the integration of a stacked long short-term memory (S-LSTM) network to incorporate the historical interaction information and environmental changes among agents. Finally, the experimental results demonstrate that the proposed MATD3-Stacked_LSTM algorithm can effectively improve the efficiency and practicality of UAV path planning by achieving a high coverage rate of the target area and reducing the redundant coverage rate among UAVs compared with two other advanced DRL algorithms.

Suggested Citation

  • Qianqian Wu & Qiang Liu & Zefan Wu & Jiye Zhang, 2023. "Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach," Future Internet, MDPI, vol. 15(11), pages 1-19, November.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:11:p:369-:d:1281158
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    References listed on IDEAS

    as
    1. Siyu Gao & Yuchen Wang & Nan Feng & Zhongcheng Wei & Jijun Zhao, 2023. "Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks," Future Internet, MDPI, vol. 15(5), pages 1-19, May.
    2. Zhiqiang Dai & Gaochao Xu & Ziqi Liu & Jiaqi Ge & Wei Wang, 2022. "Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning," Future Internet, MDPI, vol. 14(8), pages 1-19, July.
    3. Sang-Yoon Chang & Kyungmin Park & Jonghyun Kim & Jinoh Kim, 2023. "Securing UAV Flying Base Station for Mobile Networking: A Review," Future Internet, MDPI, vol. 15(5), pages 1-14, May.
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