AI-Driven Cloud Analytics and Hardware-Assisted Edge Intelligence for Real-Time Cyber-Physical Infrastructure Management

Abstract

Real-time monitoring and smart decision-making are required in cyber-physical infrastructures such as smart grids, transportation systems, and industrial automation systems to ensure process efficiency and system resilience. However, higher latency, bandwidth constraints, and a lack of responsiveness to time-sensitive data streams haunt traditional cloud-based architectures. To address these challenges, a coherent framework for integrating AI-based cloud analytics with edge intelligent hardware for managing cyber-physical infrastructure is proposed in the following paper. The architecture is based on distributed edge nodes, with hardware accelerators for very low-latency inference, and cloud layers that perform large-scale analytics and optimisation of global models. A hybrid resource allocation strategy is a self-regulating plan for the allocation of computing workloads across cloud and edge environments. Also, an adaptive learning mechanism improves prediction accuracy in changing operational environments. The framework is mathematically designed to achieve optimal latency, throughput, and computational efficiency. The proposed system reduces latency by 145ms to 92 (≈36.5) units and increases prediction accuracy from 81.2% to 96.4% when applied to a dynamic workload. The convergence analysis shows that standardized quicker convergence occurs after 35 iterations as opposed to 60 iterations in the models at the baseline. Additionally, the throughput is proceeding at 520 requests to 780 requests and error rates are falling by a factor of around 41, ensuring better reliability. Relative performance analysis across various scenarios indicates consistent improvement in both edge-dominant and cloud-dominant setups. The results of this study demonstrate that, when combined with hardware-based edge intelligence, AI-powered cloud analytics can significantly enhance the responsiveness, scalability, and decision accuracy in cyber-physical infrastructure systems.