Document Type
Journal Article
Publication Title
PLoS ONE
Volume
18
Issue
2 February
PubMed ID
36827390
Publisher
PLOS
School
School of Engineering
RAS ID
55049
Funders
Deanship of Scientific Research, King Khalid University - Grant RGP.2/201/43
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.
DOI
10.1371/journal.pone.0280468
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Xing, Y., Ye, T., Ullah, S., Waqas, M., Alasmary, H., & Liu, Z. (2023). A computational offloading optimization scheme based on deep reinforcement learning in perceptual network. Plos One, 18(2), Article e0280468. https://doi.org/10.1371/journal.pone.0280468