The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916157341040640 |
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| author | Xu, Jingyu Wan, Weixiang Pan, Linying Sun, Wenjian Liu, Yuxiang |
| author_facet | Xu, Jingyu Wan, Weixiang Pan, Linying Sun, Wenjian Liu, Yuxiang |
| contents | In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and outputs controls at a high frequency, enabling monitoring and optimization of industrial objectives. Additionally, a dynamic resource allocation mechanism is designed to ensure rational scheduling of edge computing resources, achieving global optimization. Results demonstrate that this approach reduces cloud-edge communication latency, accelerates response to abnormal situations, reduces system failure rates, extends average equipment operating time, and saves costs for manual maintenance and replacement. This ensures real-time and stable control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07923 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments Xu, Jingyu Wan, Weixiang Pan, Linying Sun, Wenjian Liu, Yuxiang Networking and Internet Architecture Artificial Intelligence Machine Learning Systems and Control Image and Video Processing In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and outputs controls at a high frequency, enabling monitoring and optimization of industrial objectives. Additionally, a dynamic resource allocation mechanism is designed to ensure rational scheduling of edge computing resources, achieving global optimization. Results demonstrate that this approach reduces cloud-edge communication latency, accelerates response to abnormal situations, reduces system failure rates, extends average equipment operating time, and saves costs for manual maintenance and replacement. This ensures real-time and stable control. |
| title | The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning Systems and Control Image and Video Processing |
| url | https://arxiv.org/abs/2403.07923 |