Intelligent Edge Computing Architecture for Real-Time Smart Factory Operations

Abstract

The rapid advancement of Industry 4.0 has accelerated the development of intelligent and autonomous smart factories powered by Industrial Internet of Things (IIoT) devices, cyber-physical systems (CPS), and advanced manufacturing technologies. Although cloud computing offers significant computational and storage capabilities, it suffers from latency, bandwidth limitations, privacy concerns, and delayed decision-making in time-critical industrial environments. This paper proposes an Integrated Intelligent Edge Computing Architecture for Real-Time Smart Factory Operations, combining edge computing, Artificial Intelligence (AI), digital twins, and predictive analytics to enable real-time local data processing with seamless cloud integration. The framework employs machine learning for anomaly detection, deep learning for automated quality inspection, reinforcement learning for adaptive production scheduling, and predictive models for equipment health monitoring. Secure communication protocols enhance data protection and system reliability. Experimental results demonstrate improved latency, prediction accuracy, manufacturing efficiency, energy utilization, fault detection, and operational resilience compared with conventional cloud-based approaches. The proposed architecture provides a scalable and sustainable solution for next-generation autonomous smart factories, improving equipment reliability, reducing operational costs, and increasing manufacturing productivity.