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Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning

Author

Listed:
  • Zhiqiang Dai

    (Department of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Gaochao Xu

    (Department of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Ziqi Liu

    (Department of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Jiaqi Ge

    (Department of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Wei Wang

    (Department of Computer Science and Technology, Jilin University, Changchun 130012, China)

Abstract

Unmanned aerial vehicles (UAVs) have the characteristics of portability, safety, and strong adaptability. In the case of a maritime disaster, they can be used for personnel search and rescue, real-time monitoring, and disaster assessment. However, the power, computing power, and other resources of UAVs are often limited. Therefore, this paper combines a UAV and mobile edge computing (MEC), and designs a deep reinforcement learning-based online task offloading (DOTO) algorithm. The algorithm can obtain an online offloading strategy that maximizes the residual energy of the UAV by jointly optimizing the UAV’s time and communication resources. The DOTO algorithm adopts time division multiple access (TDMA) to offload and schedule the UAV computing task, integrates wireless power transfer (WPT) to supply power to the UAV, calculates the residual energy corresponding to the offloading action through the convex optimization method, and uses an adaptive K method to reduce the computational complexity of the algorithm. The simulation results show that the DOTO algorithm proposed in this paper for the energy-saving goal of maximizing the residual energy of UAVs in MEC can provide the UAV with an online task offloading strategy that is superior to other traditional benchmark schemes. In particular, when an individual UAV exits the system due to insufficient power or failure, or a new UAV is connected to the system, it can perform timely and automatic adjustment without manual participation, and has good stability and adaptability.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:226-:d:872248
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    References listed on IDEAS

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    1. Zhiyan Yu & Gaochao Xu & Yang Li & Peng Liu & Long Li, 2021. "Joint Offloading and Energy Harvesting Design in Multiple Time Blocks for FDMA Based Wireless Powered MEC," Future Internet, MDPI, vol. 13(3), pages 1-23, March.
    2. Long Li & Gaochao Xu & Peng Liu & Yang Li & Jiaqi Ge, 2020. "Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System," Future Internet, MDPI, vol. 12(12), pages 1-18, December.
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    Cited by:

    1. 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.

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