Real-Time Edge Computing Framework for IIoT-Driven Industrial Automation

Abstract

As the industrial automation environment changes, people will need to perform real-time data analysis and make quick decisions. This paper proposes a highly scalable edge computing platform to meet the demanding requirements of the Industrial Internet of Things (IIoT) for high-load workloads, device diversity, and low-latency access. In this framework, prediction, resource-aware scheduling, and dynamic task offloading are the three levels. In the experiments, an industrial-grade hardware testing platform will be used to test all environments. According to the above results, the proposed system achieves an end-to-end latency of less than 20 milliseconds, a 25% increase in peak load throughput, and a 46% reduction in bandwidth consumption for edge-to-cloud transmission compared to traditional methods. The system performs well under resource constraints and is relatively resilient to network and device failures. This article provides theoretical support and practical examples to help establish intelligent, reliable, and efficient industrial automation at the network edge. The above results can be used for the development of future autonomous industrial platforms and next-generation digital factories.