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

Published

May 1, 2020

Author(s)

Hansong Xu, Xing Liu, Wei Yu, David W. Griffith, Nada T. Golmie

Abstract

Industrial Internet-of-Things (I-IoT), 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 largely improved. From a cyber physical system (CPS) perspective, there are several cyber systems that are synthesized into I-IoT systems interactively towards achieving the design goals, including control, networking, and computing systems. The interactions among different systems is a non- negligible factor that affects the I-IoT design and I-IoT 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 reconfigure the control and networking systems automatically in a dynamic industrial environment.

Citation

IEEE Journal on Selected Areas in Communications

Volume

38

Issue

5

Pub Type

Journals

Download Paper

https://doi.org/10.1109/JSAC.2020.2980909

Local Download

Citation

Xu, H. , Liu, X. , Yu, W. , Griffith, D. and Golmie, N. (2020), Reinforcement Learning-Based Control and Networking Co-design for Industrial Internet of Things, IEEE Journal on Selected Areas in Communications, [online], https://doi.org/10.1109/JSAC.2020.2980909, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928205 (Accessed April 18, 2024)