Digital Twin-assisted Task Offloading Algorithms for the Industrial Internet of Things

TANG Lun; SHAN Zhenzhen; WEN Mingyan; LI Li; CHEN Qianbin

doi:10.11999/JEIT230317

Volume 46 Issue 4

Apr. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(4): 1296-1305

TANG Lun, SHAN Zhenzhen, WEN Mingyan, LI Li, CHEN Qianbin. Digital Twin-assisted Task Offloading Algorithms for the Industrial Internet of Things[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1296-1305. doi: 10.11999/JEIT230317

Citation:

TANG Lun, SHAN Zhenzhen, WEN Mingyan, LI Li, CHEN Qianbin. Digital Twin-assisted Task Offloading Algorithms for the Industrial Internet of Things[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1296-1305. doi: 10.11999/JEIT230317

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Digital Twin-assisted Task Offloading Algorithms for the Industrial Internet of Things

doi: 10.11999/JEIT230317

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of mobile Communications Technology, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62071078), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601), Sichuan and Chongqing Key R&D Projects (2021YFQ0053), The Natural Science Project of Guizhou Provincial Department of Education (QJHKYZ[2021]236)

Received Date: 2023-04-26
Rev Recd Date: 2024-02-28

Available Online: 2024-03-08

Publish Date: 2024-04-24

Abstract

Abstract

To address the low efficiency of task collaboration computation caused by limited resources of Industrial Internet of Things (IIoT) devices and dynamic changes of edge server resources, a Digital Twin (DT)-assisted task offloading algorithm is proposed for IIoT. First, the cloud-edge-end three-layer digital twin-assisted task offloading framework is constructed by the algorithm, and the approximate optimal task offloading strategy is generated in the created digital twin layer. Second, under the constraints of task computation time and energy, the joint optimization problem of user association and task partition in the computation offloading process is studied from the perspective of delay. An optimization model is established with the goal of minimizing the task offloading time and service failure penalty. Finally, a user association and task partition algorithm based on Deep Multi-Agent Parameterized Q-Network (DMAPQN) is proposed. The approximate optimal user association and task partition strategy is obtained by each intelligent agent through continuous exploration and learning, and it is issued to the physical entity network for execution. Simulation results show that the proposed task offloading algorithm effectively reduces the task collaboration computation time and provides approximate optimal offloading strategies for each computational task.
- Industrial Internet of Things (IIoT),
- Digital Twins (DT),
- Edge association,
- Division of tasks,
- Deep reinforcement learning

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References(17)

References

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