Research on computing offloading methods based on edge computing and reinforcement learning in the industrial Internet of Things

Xian Wang; Qiang Wan; JianCheng Li

doi:10.1117/12.3013740

29 November 2023 Research on computing offloading methods based on edge computing and reinforcement learning in the industrial Internet of Things

Xian Wang, Qiang Wan, JianCheng Li

Author Affiliations +

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129370U (2023) https://doi.org/10.1117/12.3013740
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

Most of the existing research focuses on a single MEC server scenario, and there are few studies on computing offloading and resource allocation in multi-MEC server scenarios. Therefore, this paper proposes a dynamic computation offloading and resource allocation algorithm based on hybrid decision-making deep reinforcement learning, which utilizes deep deterministic policy gradients and dueling dual deep Q-networks to enhance the Actor-Critic structure. By calling the Actor part of DDPG to deal with continuous power allocation, and then combining the Critc part of DDPG with D3QN, the discrete MID is associated with the MEC server to solve the mixed decision-making problem in multi-MEC server multiuser scenarios. From the simulation results, we can see that the proposed algorithm has faster convergence and better stability than baseline algorithms such as DQN.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xian Wang, Qiang Wan, and JianCheng Li "Research on computing offloading methods based on edge computing and reinforcement learning in the industrial Internet of Things", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129370U (29 November 2023); https://doi.org/10.1117/12.3013740

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