Edge AI Deployment Models for Real-Time Industrial Automation Feedback

  • Authors

    • Dr. Fatima Noor Associate Professor, University of Malaya, Malaysia. Author
    • Dr. Siti Rahman Associate Professor, Universiti Kebangsaan Malaysia, Malaysia. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2019PI5W1D

    Published 04-02-2019

  • Edge AI, Real-Time Feedback, Industrial Automation, Edge Computing, Smart Manufacturing, On-Device Inference, Edge Gateway, Hybrid Deployment, Predictive Maintenance, Low-Latency AI

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    F. Noor and S. Rahman, “Edge AI Deployment Models for Real-Time Industrial Automation Feedback”, IJMLPA, vol. 2, no. 1, pp. 01–10, Apr. 2019, doi: 10.67228/3142788X/IJMLPA-2019PI5W1D.

  • Abstract

    Edge AI is revolutionizing the industrial automation landscape by enabling real-time decision-making and feedback directly at the data source. Unlike traditional cloud-centric architectures, edge AI reduces latency, enhances data privacy, and ensures uninterrupted operations even in bandwidth-constrained environments. This paper explores various deployment models of edge AI tailored for real-time industrial automation feedback systems. We analyze on-device, edge gateway, and hybrid edge-cloud approaches, discussing their architectures, benefits, limitations, and real-world applicability. Through the lens of case studies and optimization techniques, we demonstrate how edge AI fosters responsiveness and resilience in smart industrial systems. The paper also outlines challenges and future research directions in deploying scalable, secure, and efficient edge AI solutions for Industry 4.0 and beyond.

  • References

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