Real-Time Data Analytics and Decision Making in Cyber-Physical Systems

Abstract

The future of digital innovation lies in Cyber-Physical Systems (CPS), integrating computational capabilities with physical processes. CPS function as interconnected networks, merging physical and digital inputs and outputs. This study defines CPS and highlights real-time data analytics' role in enhancing communication across industries like manufacturing and robotics. CPS depend on data collection via edge computing, IoT, and sensors, involving data cleaning, preparation, and normalization. Real-time analytics, including stream processing, machine learning, and AI, are crucial for CPS. Decision-making systems and algorithms enhance efficiency. Given their sensitivity, security and privacy in real-time analytics are vital. The study addresses threat detection, privacy preservation, and data security, along with challenges like data heterogeneity, latency, and scalability. Future prospects include edge AI, fog computing, and blockchain integration.