Multiagent Deep Reinforcement Learning for Joint Multichannel Access and Task Offloading of Mobile-Edge Computing in Industry 4.0

2020 ◽  
Vol 7 (7) ◽  
pp. 6201-6213 ◽  
Author(s):  
Zilong Cao ◽  
Pan Zhou ◽  
Ruixuan Li ◽  
Siqi Huang ◽  
Dapeng Wu
Keyword(s):  
Reinforcement Learning ◽  
Industry 4.0 ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
And Task ◽  
Task Offloading ◽  
Multichannel Access

Joint wireless power transfer and task offloading in mobile edge computing: a survey

2021 ◽  
Author(s):  
Ehzaz Mustafa ◽  
Junaid Shuja ◽  
S. Khaliq uz Zaman ◽  
Ali Imran Jehangiri ◽  
Sadia Din ◽  
...  
Keyword(s):  
Wireless Power Transfer ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Power Transfer ◽  
Wireless Power ◽  
And Task ◽  
Task Offloading

scholarly journals Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 202573-202584
Author(s):  
Haifeng Lu ◽  
Chunhua Gu ◽  
Fei Luo ◽  
Weichao Ding ◽  
Shuai Zheng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Multi Agent ◽  
Task Offloading

scholarly journals Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 54074-54084 ◽  
Author(s):  
Taha Alfakih ◽  
Mohammad Mehedi Hassan ◽  
Abdu Gumaei ◽  
Claudio Savaglio ◽  
Giancarlo Fortino
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Offloading

scholarly journals Research on task offloading based on deep reinforcement learning in mobile edge environment

2020 ◽  
Vol 309 ◽  
pp. 03026
Author(s):  
Xia Gao ◽  
Fangqin Xu
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Edge Computing ◽  
Internet Technology ◽  
Good Effect ◽  
Mobile Edge Computing ◽  
Data Set ◽  
The Impact ◽  
Task Offloading ◽  
Reinforcement Learning Algorithm

With the rapid development of Internet technology and mobile terminals, users’ demand for high-speed networks is increasing. Mobile edge computing proposes a distributed caching approach to deal with the impact of massive data traffic on communication networks, in order to reduce network latency and improve user service quality. In this paper, a deep reinforcement learning algorithm is proposed to solve the task unloading problem of multi-service nodes. The simulation platform iFogSim and data set Google Cluster Trace are used to carry out experiments. The final results show that the task offloading strategy based on DDQN algorithm has a good effect on energy consumption and cost, it has verified the application prospect of deep reinforcement learning algorithm in mobile edge computing.

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