Enhancing SCADA Systems with Multi-access Edge Computing and Hierarchical Dirichlet Processes for Real-Time Data Analytics

Abstract

Background Traditional SCADA systems struggle to handle the growing complexity and real- time data requirements of modern industrial processes powered by IoT sensors. Methods The research uses Multi-access Edge Computing (MEC) and Hierarchical Dirichlet Processes (HDPs) to enhance SCADA systems' real-time data analytics and anomaly detection capabilities. Objectives The goal is to improve SCADA systems by lowering latency, enhancing predictive maintenance, and allowing for dynamic, real-time data processing and decision-making in important industrial applications. Results The proposed system surpassed previous approaches with 92% accuracy, 90% efficiency, and 93% scalability, while also reducing latency by 95%, making it perfect for real- time industrial operations. Conclusion Integrating MEC and HDPs into SCADA systems converts them into adaptive, efficient platforms capable of real-time analytics, which improves predictive maintenance and operational efficiency in a variety of industrial environments.