Latency-Aware Edge Computing Architecture for Real-Time Industrial Automation

The rapid proliferation of Industrial Internet of Things (IIoT) and Industry 4.0 initiatives has transformed traditional manufacturing into interconnected systems requiring ultra-low latency and real-time processing. This research proposes a latency-aware edge computing architecture optimized for real-time industrial automation. The architecture leverages edge nodes integrated with intelligent workload distribution, latency prediction models, and dynamic scheduling mechanisms. Experimental validation using regression and predictive analysis demonstrates significant improvements in latency reduction, throughput, and system responsiveness compared to cloud-centric models. The study contributes a novel, deployable framework that aligns with critical industrial requirements, including fault tolerance, scalability, and Quality of Service (QoS). Keywords Edge Computing, Real-Time Systems, Industrial Automation, Latency Prediction, IIoT, Regression Analysis, Architecture Optimization