REAL-TIME EDGE INTELLIGENCE FOR INDUSTRIAL AUTOMATION

Authors

  • Meraj 2nd Semester, Department of Master of Computer Applications, Gurunanak Dev Engineering College, Bidar, India
  • Sainath 2nd Semester, Department of Master of Computer Applications, Guru Nanak Dev Engineering College, Bidar, India
  • Neha 2nd Semester, Department of Master of Computer Applications, Guru Nanak Dev Engineering College, Bidar, India
  • Qudsiya Shaila 2nd Semester, Department of Master of Computer Applications, Guru Nanak Dev Engineering College, Bidar, India

Keywords:

Edge Computing, Industrial Automation, Artificial Intelligence (AI), Machine Learning (ML), Edge Intelligence, Real-Time Processing, Industrial IoT (IoT), Predictive Maintenance, Anomaly Detection, Smart Manufacturing, Cyber-Physical Systems, Low Latency Systems

Abstract

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.

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Additional Files

Published

01-06-2026

How to Cite

Meraj, Sainath, Neha, & Qudsiya Shaila. (2026). REAL-TIME EDGE INTELLIGENCE FOR INDUSTRIAL AUTOMATION. International Educational Journal of Science and Engineering, 9(05), 664–670. Retrieved from https://iejse.com/journals/index.php/iejse/article/view/408

Issue

Vol. 9 No. 05 (2026): SPECIAL ISSUE MAY - NATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING, TECHNOLOGY AND MANAGEMENT

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Articles

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