Edge AI Deploying Artificial Intelligence Models on Edge Devices for Real-Time Analytics
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 a...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_01009.pdf |
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| author | Choudhary Sagar S Vijitha Bhavani Dokku Durga N Bhuvaneswari Tiwari Mohit S Subburam |
| author_facet | Choudhary Sagar S Vijitha Bhavani Dokku Durga N Bhuvaneswari Tiwari Mohit S Subburam |
| author_sort | Choudhary Sagar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-88f382e6d1a548058da2966e2db70940 |
| institution | DOAJ |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-88f382e6d1a548058da2966e2db709402025-08-20T03:04:30ZengEDP SciencesITM Web of Conferences2271-20972025-01-01760100910.1051/itmconf/20257601009itmconf_icsice2025_01009Edge AI Deploying Artificial Intelligence Models on Edge Devices for Real-Time AnalyticsChoudhary Sagar0S Vijitha1Bhavani Dokku Durga2N Bhuvaneswari3Tiwari Mohit4S Subburam5Assistant Professor, Department of Mathematics Bio Informatics and Computer Application, MANITAssistant Professor, Vels Institute of Science, Technology & Advanced StudiesProfessor, Department of Computer Science and Engineering, CVR College of EngineeringAssistant Professor, Department of CSE, Nandha EngineeringAssistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of EngineeringProfessor, Department of IT, New Prince Shri Bhavani College of Engineering and TechnologyBecause 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.https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_01009.pdfedge aireal-time analyticsartificial intelligenceenergy efficiencyfederated learningexplainable aiedge-cloud integrationcybersecuritymulti-modal aiautonomous systemssmart citieshealthcare aisustainable computingmodel specializationadaptive ai |
| spellingShingle | Choudhary Sagar S Vijitha Bhavani Dokku Durga N Bhuvaneswari Tiwari Mohit S Subburam Edge AI Deploying Artificial Intelligence Models on Edge Devices for Real-Time Analytics ITM Web of Conferences 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 |
| title | Edge AI Deploying Artificial Intelligence Models on Edge Devices for Real-Time Analytics |
| title_full | Edge AI Deploying Artificial Intelligence Models on Edge Devices for Real-Time Analytics |
| title_fullStr | Edge AI Deploying Artificial Intelligence Models on Edge Devices for Real-Time Analytics |
| title_full_unstemmed | Edge AI Deploying Artificial Intelligence Models on Edge Devices for Real-Time Analytics |
| title_short | Edge AI Deploying Artificial Intelligence Models on Edge Devices for Real-Time Analytics |
| title_sort | edge ai deploying artificial intelligence models on edge devices for real time analytics |
| topic | 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 |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_01009.pdf |
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