Skip to main content
placeholder image

Reinforcement Learning-Based Control and Networking Co-Design for Industrial Internet of Things

Journal Article


Abstract


  • Industrial Internet-of-Things (IIoT), also known as Industry 4.0, is the integration of Internet of Things (IoT) technology into the industrial manufacturing system so that the connectivity, efficiency, and intelligence of factories and plants can be improved. From a cyber physical system (CPS) perspective, multiple systems (e.g., control, networking and computing systems) are synthesized into IIoT systems interactively to achieve the operator's design goals. The interactions among different systems is a non-negligible factor that affects the IIoT design and requirements, such as automation, especially under dynamic industrial operations. In this paper, we leverage reinforcement learning techniques to automatically configure the control and networking systems under a dynamic industrial environment. We design three new policies based on the characteristics of industrial systems so that the reinforcement learning can converge rapidly. We implement and integrate the reinforcement learning-based co-design approach on a realistic wireless cyber-physical simulator to conduct extensive experiments. Our experimental results demonstrate that our approach can effectively and quickly reconfigure the control and networking systems automatically in a dynamic industrial environment.

Publication Date


  • 2020

Citation


  • Xu, H., Liu, X., Yu, W., Griffith, D., & Golmie, N. (2020). Reinforcement Learning-Based Control and Networking Co-Design for Industrial Internet of Things. IEEE Journal on Selected Areas in Communications, 38(5), 885-898. doi:10.1109/JSAC.2020.2980909

Scopus Eid


  • 2-s2.0-85082085732

Web Of Science Accession Number


Start Page


  • 885

End Page


  • 898

Volume


  • 38

Issue


  • 5

Abstract


  • Industrial Internet-of-Things (IIoT), also known as Industry 4.0, is the integration of Internet of Things (IoT) technology into the industrial manufacturing system so that the connectivity, efficiency, and intelligence of factories and plants can be improved. From a cyber physical system (CPS) perspective, multiple systems (e.g., control, networking and computing systems) are synthesized into IIoT systems interactively to achieve the operator's design goals. The interactions among different systems is a non-negligible factor that affects the IIoT design and requirements, such as automation, especially under dynamic industrial operations. In this paper, we leverage reinforcement learning techniques to automatically configure the control and networking systems under a dynamic industrial environment. We design three new policies based on the characteristics of industrial systems so that the reinforcement learning can converge rapidly. We implement and integrate the reinforcement learning-based co-design approach on a realistic wireless cyber-physical simulator to conduct extensive experiments. Our experimental results demonstrate that our approach can effectively and quickly reconfigure the control and networking systems automatically in a dynamic industrial environment.

Publication Date


  • 2020

Citation


  • Xu, H., Liu, X., Yu, W., Griffith, D., & Golmie, N. (2020). Reinforcement Learning-Based Control and Networking Co-Design for Industrial Internet of Things. IEEE Journal on Selected Areas in Communications, 38(5), 885-898. doi:10.1109/JSAC.2020.2980909

Scopus Eid


  • 2-s2.0-85082085732

Web Of Science Accession Number


Start Page


  • 885

End Page


  • 898

Volume


  • 38

Issue


  • 5