IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0280468.html
   My bibliography  Save this article

A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

Author

Listed:
  • Yongli Xing
  • Tao Ye
  • Sami Ullah
  • Muhammad Waqas
  • Hisham Alasmary
  • Zihui Liu

Abstract

Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms.

Suggested Citation

  • Yongli Xing & Tao Ye & Sami Ullah & Muhammad Waqas & Hisham Alasmary & Zihui Liu, 2023. "A computational offloading optimization scheme based on deep reinforcement learning in perceptual network," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-19, February.
    • Handle: RePEc:plo:pone00:0280468
    DOI: 10.1371/journal.pone.0280468
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0280468
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0280468&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0280468?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0280468. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.