Reinforcement Learning for Adaptive Sampling: Managing Network Resources in Cloud-Based Cyber-Physical Systems

This paper presents a novel approach to optimizing sampling frequency in cloud-based cyber-physical systems (CPS) using reinforcement learning (RL). We address the critical challenge of balancing control performance with network resource utilization in scenarios where the controller is deployed in t...

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Bibliographic Details
Published inProceedings of IEEE Southeastcon pp. 1136 - 1141
Main Authors Nazrul, Samiha, Rahman, Mohammad, Wu, Fan
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.03.2025
Subjects
Adaptive Control
Cloud-based Cyber-Physical Systems (CBCPS)
Cyber-physical systems
Network Congestion Management
Networked Control Systems (NCS)
Optimization
Reinforcement learning
Reinforcement Learning (RL)
Resource management
Steady-state
System performance
Testing
Trajectory
Transient analysis
Velocity control
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Summary:This paper presents a novel approach to optimizing sampling frequency in cloud-based cyber-physical systems (CPS) using reinforcement learning (RL). We address the critical challenge of balancing control performance with network resource utilization in scenarios where the controller is deployed in the cloud and subject to variable network conditions. Our proposed solution employs a Proximal Policy Optimization (PPO) algorithm to dynamically select from discrete sampling frequencies based on system state and network conditions. Using a vehicle speed control system as a case study, we demonstrate that our RL-based approach significantly improves control performance while efficiently managing network resources. Results show smoother system response and reduced network congestion compared to random frequency selection strategies.
ISSN:1558-058X
DOI:10.1109/SoutheastCon56624.2025.10971596