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

Area of Science:

• Computer Science
• Engineering
• Automation

Background:

• Industrial Internet-of-Things (IIoT), or Industry 4.0, integrates Internet of Things (IoT) into manufacturing for improved connectivity and efficiency.
• Cyber-Physical Systems (CPS) in IIoT involve interactive synthesis of control, networking, and computing systems.
• Dynamic industrial operations present challenges for IIoT system design and automation due to complex system interactions.

Purpose of the Study:

• To leverage reinforcement learning (RL) for automated configuration of control and networking systems in dynamic industrial environments.
• To develop novel RL policies tailored for industrial system characteristics to ensure rapid convergence.
• To validate the RL-based co-design approach through extensive simulations on a wireless cyber-physical system.

Main Methods:

• Utilized reinforcement learning techniques for adaptive system configuration.
• Designed three new RL policies optimized for industrial system dynamics and rapid convergence.
• Implemented and tested the RL-based co-design on a realistic wireless cyber-physical simulator.

Main Results:

• The RL-based approach demonstrated effective and rapid automatic reconfiguration of control and networking systems.
• Experimental results confirmed the approach's capability to handle dynamic industrial environments.
• The designed policies facilitated faster convergence of the RL algorithms.

Conclusions:

• The proposed reinforcement learning-based co-design method successfully automates IIoT system configuration in dynamic industrial settings.
• The approach offers a promising solution for enhancing automation and adaptability in Industry 4.0.
• The study validates the efficacy of tailored RL policies for complex cyber-physical system optimization.