Machine Learning–Driven Real-Time Monitoring and Control of Cyber-Physical Systems Using Edge Gateways and Cloud Networks
DOI:
https://doi.org/10.63503/j.ijaimd.2026.262Keywords:
Cyber-Physical Systems (CPS), Machine Learning, Edge Computing, Cloud Networks, Real-Time Monitoring, Intelligent Control, Anomaly Detection, Distributed Systems.Abstract
The growing use of cyber-physical systems (CPS) in business processes such as smart manufacturing, healthcare, transport, and energy has raised major concerns regarding real-time monitoring, control, scalability, and system trustworthiness. The established centralised cloud-based applications are characterised by low responsiveness and high latency (usually 4060 ms), whereas systems that are based solely on the edges do not have the intelligence and coordination across the globe. The following paper will propose a machine-learning-based system to monitor and control CPS in real time, with very low decision-making latency, and to perform scaled system intelligence using edge gateways and cloud networks. In the suggested scheme, edge gateways will process real-time data, identify anomalies, implement control measures using lightweight machine learning models, coordinate their operations globally, optimise their models, and conduct long-term analytics, all provided through the cloud layer. The system has been able to promote distributed learning without disseminating raw data, hence enhancing efficiency and privacy. The comparison of the proposed method, based on the experimental evaluation of over 100 monitoring cycles, shows that the necessary detection accuracy (approximately 91%), compared to the centralised (approximately 80%) and edge-only (approximately 72%) methods, is rather good. Also, response latency decreases to 21 ms from 48 ms, while communication overhead remains at about 85 MB per cycle, making deployment practical and scalable.
References
[1] Jahromi, A. A., & Kundur, D. (2020). Fundamentals of cyber-physical systems. In Cyber-physical systems in the built environment (pp. 1-13). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-41560-0_1
[2] Yao, X., Zhou, J., Lin, Y., Li, Y., Yu, H., & Liu, Y. (2019). Smart manufacturing based on cyber-physical systems and beyond. Journal of Intelligent Manufacturing, 30(8), 2805-2817. https://doi.org/10.1007/s10845-017-1384-5
[3] Jiang, Y., Yin, S., & Kaynak, O. (2018). Data-driven monitoring and safety control of industrial cyber-physical systems: Basics and beyond. IEEe Access, 6, 47374-47384. doi: 10.1109/ACCESS.2018.2866403.
[4] Gai, K., Qiu, M., Zhao, H., & Sun, X. (2017). Resource management in sustainable cyber-physical systems using heterogeneous cloud computing. IEEE Transactions on Sustainable Computing, 3(2), 60-72. doi: 10.1109/TSUSC.2017.2723954.
[5] Habib, M. K., & Chimsom, C. (2022). CPS: Role, characteristics, architectures and future potentials. Procedia Computer Science, 200, 1347-1358. https://doi.org/10.1016/j.procs.2022.01.336
[6] Yin, S., Bao, J., Li, J., & Zhang, J. (2019). Real-time task processing method based on edge computing for spinning CPS. Frontiers of Mechanical Engineering, 14(3), 320-331. https://doi.org/10.1007/s11465-019-0542-1
[7] Wang, H. (2020). Research on real-time reliability evaluation of CPS system based on machine learning. Computer communications, 157, 336-342. https://doi.org/10.1016/j.comcom.2020.04.039
[8] Chen, C., Li, Y., Wang, Q., Yang, X., Wang, X., & Yang, L. T. (2023). An intelligent edge-cloud collaborative framework for communication security in distributed cyber-physical systems. IEEE Network, 38(1), 172-179. doi: 10.1109/MNET.2023.3321923.
[9] Mitchell, R., & Chen, R. (2014). Behavior rule specification-based intrusion detection for safety critical medical cyber physical systems. IEEE Transactions on Dependable and Secure Computing, 12(1), 16-30. doi: 10.1109/TDSC.2014.2312327.
[10] Park, J., Lee, S., Yoon, T., & Kim, J. M. (2015). An autonomic control system for high-reliable CPS. Cluster Computing, 18(2), 587-598. https://doi.org/10.1007/s10586-014-0414-8
[11] Zhang, N. (2021). A cloud-based platform for big data-driven cps modeling of robots. IEEE Access, 9, 34667-34680. doi: 10.1109/ACCESS.2021.3061477.
[12] Qian, C., Guo, Y., Liang, H., Song, J., & Yu, W. (2025). Secured edge intelligence in smart manufacturing CPS. In Edge Intelligence in Cyber-Physical Systems (pp. 377-401). Academic Press. https://doi.org/10.1016/B978-0-44-326572-3.00024-3
[13] Reddy, S. S., Nrusimhadri, S., Mahesh, G., & Rao, V. V. R. M. (2025). A Dependency-Aware task offloading in IoT-based Edge Computing system using an Optimized Deep Learning Approach. Parallel Computing, 103161. https://doi.org/10.1016/j.parco.2025.103161
[14] Corli, S., Moro, L., Dragoni, D., Dispenza, M., & Prati, E. (2025). Quantum machine learning algorithms for anomaly detection: A review. Future Generation Computer Systems, 166, 107632. https://doi.org/10.1016/j.future.2024.107632
[15] Song, C., Zhang, X., She, Y., Li, B., & Zhang, Q. (2025). Trajectory planning for uav swarm tracking moving target based on an improved model predictive control fusion algorithm. IEEE Internet of Things Journal. doi: 10.1109/JIOT.2025.3541298.
[16] Larian, H., & Safi-Esfahani, F. (2025). InTec: integrated things-edge computing: a framework for distributing machine learning pipelines in edge AI systems. Computing, 107(1), 41. https://doi.org/10.1007/s00607-024-01388-6
[17] Qian, C., Guo, Y., Liang, H., Song, J., & Yu, W. (2025). Secured edge intelligence in smart manufacturing CPS. In Edge Intelligence in Cyber-Physical Systems (pp. 377-401). Academic Press. https://doi.org/10.1016/B978-0-44-326572-3.00024-3
[18] https://www.kaggle.com/datasets/mohamedamineferrag/edgeiiotset-cyber-security-dataset-of-iot-iiot
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