| Issue |
ITM Web Conf.
Volume 76, 2025
Harnessing Innovation for Sustainability in Computing and Engineering Solutions (ICSICE-2025)
|
|
|---|---|---|
| Article Number | 01009 | |
| Number of page(s) | 9 | |
| Section | Artificial Intelligence & Machine Learning | |
| DOI | https://doi.org/10.1051/itmconf/20257601009 | |
| Published online | 25 March 2025 | |
Edge AI Deploying Artificial Intelligence Models on Edge Devices for Real-Time Analytics
1 Assistant Professor, Department of Mathematics Bio Informatics and Computer Application, MANIT, Bhopal, India
2 Assistant Professor, Vels Institute of Science, Technology & Advanced Studies, Pallavaram, Chennai, Tamil Nadu, India
3 Professor, Department of Computer Science and Engineering, CVR College of Engineering, Hyderabad, Telengana, India
4 Assistant Professor, Department of CSE, Nandha Engineering, Vaaikalmedu, Erode, Tamil Nadu, India
5 Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, A-4, Rohtak Road, Paschim Vihar, Delhi, India
6 Professor, Department of IT, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India
sagarc@manit.ac.in
vijisri.s27@gmail.com
drddurgabhavani@gmail.com
bhuvaneswari@nandhaengg.org
mohit.t.bvcoe@gmail.com
hodit@newprinceshribhavai.com
Because of its on-the-go nature, edge AI has gained popularity, allowing for realtime analytics by deploying artificial intelligence models onto edge devices. Despite the promise of Edge AI evidenced by existing research, there are still significant barriers to widespread adoption with issues such as scalability, energy efficiency, security, and reduced model explainability representing common challenges. Hence, while this paper solves the Edge AI in a number of ways, with real use case of a deployment, modular adaptability, and dynamic AI model specialization. Our paradigm achieves low latency, better security and energy efficiency using light-weight AI models, federated learning, Explainable AI (XAI) and smart edge-cloud orchestration. This framework could enable generic AI beyond specific applications that depend on multi-modal data processing, which contributes to the generalization of applications across various industries such as healthcare, autonomous systems, smart cities, and cybersecurity. Moreover, this work will help deploy sustainable AI by employing green computing techniques to detect anomalies in near real-time in various critical domains helping to ease challenges of the modern world.
Key words: Edge AI / real-time analytics / artificial intelligence / energy efficiency / federated learning / Explainable AI / edge-cloud integration / cybersecurity / multi-modal AI / autonomous systems / smart cities / healthcare AI / sustainable computing / model specialization / adaptive AI
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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