A computational offloading optimization scheme based on deep reinforcement learning in perceptual network.
PLoS One
; 18(2): e0280468, 2023.
Article
en En
| MEDLINE
| ID: mdl-36827390
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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Internet
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
PLoS One
Asunto de la revista:
CIENCIA
/
MEDICINA
Año:
2023
Tipo del documento:
Article
País de afiliación:
China