Document
A computational offloading optimization scheme based on deep reinforcement learning in perceptual network
Linked Agent
Ye, Tao , Author
Ullah, Sami , Author
Waqas, Muhammad , Author
Alasmary, Hisham , Author
Liu, Zihui , Author
Title of Periodical
PLOS ONE
Publisher
The Authors
Date Issued
2023
Language
English
English Abstract
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.
Member of
Identifier
https://digitalrepository.uob.edu.bh/id/2c02f220-327f-4582-b395-32939bfc24a1