IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i8p226-d872248.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/8/226/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/14/8/226/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniele Tarchi & Arash Bozorgchenani & Mulubrhan Desta Gebremeskel, 2022. "Zero-Energy Computation Offloading with Simultaneous Wireless Information and Power Transfer for Two-Hop 6G Fog Networks," Energies, MDPI, vol. 15(5), pages 1-24, February.
    2. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:226-:d:872248. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.