Cloud–Edge Integrated Machine Learning Framework for Real-Time Monitoring of Cyber-Physical Systems with IoT Sensor Networks

Abstract

The CPS, in combination with the IoT sensor networks, has experienced massive growth, which results in massive data generation per second that presents extreme challenges to latency, scalability, and efficient data processing. The current paper presents a cloud-edge-integrated machine learning system for real-time monitoring of CPS environments. The suggested system combines IoT data collection, edge processing, and cloud-based model optimisation to enable fast, intelligent decision-making. Edge computing reduces communication overhead by performing local inference, while the cloud provides large-scale analytics and model training. The evaluation of the framework is conducted on a dataset of 10,000 sensor records that represent industrial parameters such as temperature, pressure, and vibration. The experimental findings showed that prediction accuracy was 91.2%, processing efficiency was 88.5%, and stability was 0.86, with a much lower latency of 205 ms. The overall performance index of 0.88 indicates that the computer's responsiveness, scalability, and efficiency have improved equally. The comparative analysis demonstrates that the proposed approach is significantly superior to traditional and standalone machine learning models, which is why it can be widely applied in real-time CPS monitoring applications.