Edge Computing Enabled Cyber-Physical Systems for Real-Time Monitoring in Autonomous Industrial Environments

Abstract

Industrial automation has experienced rapid development with the emergence of cyber-physical systems and intelligent robotics. Autonomous industrial environments require continuous monitoring of equipment, processes, and environmental parameters to ensure operational safety and efficiency. Traditional centralized cloud architectures often introduce latency and bandwidth limitations that restrict real-time responsiveness in industrial applications. Edge computing has emerged as a promising solution by bringing computational capabilities closer to data sources. This research proposes an edge computing enabled cyber- physical system architecture designed to support real-time monitoring in autonomous industrial environments. The proposed framework integrates distributed sensors, embedded edge processing units, and machine learning models capable of performing on-site data analysis. Data streams from industrial sensors are processed locally using edge nodes to detect anomalies and generate alerts without relying solely on centralized cloud servers. A layered architecture consisting of sensing infrastructure, edge computing modules, communication networks, and cloud analytics platforms is introduced. The system incorporates lightweight deep learning models for anomaly detection and predictive analytics. Experimental evaluation was performed using simulated industrial monitoring scenarios involving temperature, vibration, and power consumption datasets. Results demonstrate that the edge computing architecture significantly reduces response latency and network congestion while maintaining high monitoring accuracy. The proposed system achieved a latency reduction of approximately 35 percent and improved anomaly detection accuracy compared with traditional cloud-based monitoring frameworks. The findings highlight the importance of edge-enabled cyber-physical systems for enhancing reliability, safety, and operational efficiency in modern autonomous industrial environments.