COPSA: a computation offloading strategy based on PPO algorithm and self-attention mechanism in MEC-empowered smart factories

With the dawn of Industry 5.0 upon us, the smart factory emerges as a pivotal element, playing a crucial role in the realm of intelligent manufacturing. Meanwhile, mobile edge computing is proposed to alleviate the computational burden presented by substantial workloads in smart factories. Nonetheless, it is very challenging to effectively incorporate edge computing resources to improve the efficiency of resource deployment in smart factories. Accordingly, we devise a novel approach based on Proximal Policy Optimization algorithm with the Self-Attention Mechanism to implement computing resource allocation in MEC-Empowered Smart Factories. More specifically, the self-attention mechanism is incorporated to enable dynamic focus on state information, accelerates convergence and facilitates global control. A great number of experiments conducted on both simulated and real datasets have verified the superiority of our proposed approach compared to the state-of-the-art baselines.

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Publication Details

Journal
Journal of Cloud Computing Advances Systems and Applications
Published
2024-11-05
DOI
https://doi.org/10.1186/s13677-024-00714-9
Citations
2
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
0.70

Funders

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article

COPSA: a computation offloading strategy based on PPO algorithm and self-attention mechanism in MEC-empowered smart factories

Chengfang Ling, Yining Chen, Kai Peng
2 citations
Journal of Cloud Computing Advances Systems and Applications
IoT and Edge/Fog Computing
0.70
article

COPSA: a computation offloading strategy based on PPO algorithm and self-attention mechanism in MEC-empowered smart factories

Chengfang Ling, Yining Chen, Kai Peng
article en
2 citations

Abstract

With the dawn of Industry 5.0 upon us, the smart factory emerges as a pivotal element, playing a crucial role in the realm of intelligent manufacturing. Meanwhile, mobile edge computing is proposed to alleviate the computational burden presented by substantial workloads in smart factories. Nonetheless, it is very challenging to effectively incorporate edge computing resources to improve the efficiency of resource deployment in smart factories. Accordingly, we devise a novel approach based on Proximal Policy Optimization algorithm with the Self-Attention Mechanism to implement computing resource allocation in MEC-Empowered Smart Factories. More specifically, the self-attention mechanism is incorporated to enable dynamic focus on state information, accelerates convergence and facilitates global control. A great number of experiments conducted on both simulated and real datasets have verified the superiority of our proposed approach compared to the state-of-the-art baselines.

Journal of Cloud Computing Advances Systems and ApplicationsVol. 13(1)
Huaqiao University (CN), Nanjing University (CN)
National Science Foundation, National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities, Quanzhou City Science and Technology Program
Decent work and economic growth
Openalex Percentile: Top 27%
IoT and Edge/Fog Computing
0.70
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