REAL-TIME EDGE INTELLIGENCE FOR INDUSTRIAL AUTOMATION - Meraj; Sainath; Neha; Qudsiya Shaila (International Educational Journal of Science and Engineering, 2026)
Industrial automation is undergoing a significant transformation with the growing adoption of intelligent and connected systems. However, traditional cloud-centric architectures often struggle to meet the strict latency, reliability, and bandwidth requirements of modern industrial environments. This research focuses on the development of a real-time edge intelligence framework that integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques directly into edge devices for industrial automation. The primary objective of this study is to enable faster decision-making by processing data closer to its source, thereby minimizing communication delays and reducing dependency on centralized cloud systems. The proposed approach utilizes sensor-driven data acquisition, local data preprocessing, and deployment of optimized machine learning models at the edge for real-time analytics. Key functionalities such as anomaly detection, predictive maintenance, and process optimization are implemented to enhance operational efficiency. Experimental observations indicate that the edge-based system significantly reduces latency while maintaining high accuracy in detecting faults and anomalies. Additionally, the framework demonstrates improved system reliability, reduced network congestion, and enhanced data privacy compared to conventional cloud-based solutions.