Scalable AI-Driven Cyber-Physical Systems for Secure Cloud and 5G Networks: Predictive Analytics, Reliability, and Sustainable Energy Integration

Abstract

The convergence of cloud computing, 5G networks, and cyber-physical systems (CPS) has enabled highly connected, data-intensive enterprise and critical infrastructure environments. However, this convergence also introduces challenges related to scalability, security, reliability, and energy sustainability. This paper proposes a scalable AI-driven CPS framework for secure cloud and 5G networks that integrates predictive analytics, reliability engineering, and sustainable energy management. The framework leverages machine learning and generative AI models to enable real-time monitoring, anomaly detection, predictive maintenance, and autonomous decision-making across distributed physical assets and virtual network functions. Security is embedded through zero-trust principles, AI-assisted threat intelligence, and policy-aware orchestration, while reliability is enhanced using adaptive fault prediction, redundancy optimization, and self-healing mechanisms. In parallel, energy-aware AI models optimize power consumption across cloud data centers, 5G base stations, and edge nodes by integrating renewable and sustainable energy sources. The proposed architecture supports mission-critical applications such as smart cities, industrial automation, intelligent transportation, and healthcare systems. By unifying AI-driven analytics with CPS, cloud-native architectures, and 5G networking, this work demonstrates a holistic approach to building resilient, secure, and energy-efficient digital infrastructures capable of meeting future scalability and sustainability requirements.