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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.02785 |
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| _version_ | 1866908391570407424 |
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| author | Kalalas, Charalampos Mulinka, Pavol Belmonte, Guillermo Candela Fornell, Miguel Dalgitsis, Michail Vera, Francisco Paredes Sánchez, Javier Santaella Villares, Carmen Vicente Sedar, Roshan Datsika, Eftychia Antonopoulos, Angelos Ojea, Antonio Fernández Payaro, Miquel |
| author_facet | Kalalas, Charalampos Mulinka, Pavol Belmonte, Guillermo Candela Fornell, Miguel Dalgitsis, Michail Vera, Francisco Paredes Sánchez, Javier Santaella Villares, Carmen Vicente Sedar, Roshan Datsika, Eftychia Antonopoulos, Angelos Ojea, Antonio Fernández Payaro, Miquel |
| contents | Artificial intelligence (AI) has been increasingly applied to the condition monitoring of vehicular equipment, aiming to enhance maintenance strategies, reduce costs, and improve safety. Leveraging the edge computing paradigm, AI-based condition monitoring systems process vast streams of vehicular data to detect anomalies and optimize operational performance. In this work, we introduce a novel vehicle condition monitoring service that enables real-time diagnostics of a diverse set of anomalies while remaining practical for deployment in real-world edge environments. To address mobility challenges, we propose a closed-loop service orchestration framework where service migration across edge nodes is dynamically triggered by network-related metrics. Our approach has been implemented and tested in a real-world race circuit environment equipped with 5G network capabilities under diverse operational conditions. Experimental results demonstrate the effectiveness of our framework in ensuring low-latency AI inference and adaptive service placement, highlighting its potential for intelligent transportation and mobility applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02785 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Driven Vehicle Condition Monitoring with Cell-Aware Edge Service Migration Kalalas, Charalampos Mulinka, Pavol Belmonte, Guillermo Candela Fornell, Miguel Dalgitsis, Michail Vera, Francisco Paredes Sánchez, Javier Santaella Villares, Carmen Vicente Sedar, Roshan Datsika, Eftychia Antonopoulos, Angelos Ojea, Antonio Fernández Payaro, Miquel Networking and Internet Architecture Artificial Intelligence Artificial intelligence (AI) has been increasingly applied to the condition monitoring of vehicular equipment, aiming to enhance maintenance strategies, reduce costs, and improve safety. Leveraging the edge computing paradigm, AI-based condition monitoring systems process vast streams of vehicular data to detect anomalies and optimize operational performance. In this work, we introduce a novel vehicle condition monitoring service that enables real-time diagnostics of a diverse set of anomalies while remaining practical for deployment in real-world edge environments. To address mobility challenges, we propose a closed-loop service orchestration framework where service migration across edge nodes is dynamically triggered by network-related metrics. Our approach has been implemented and tested in a real-world race circuit environment equipped with 5G network capabilities under diverse operational conditions. Experimental results demonstrate the effectiveness of our framework in ensuring low-latency AI inference and adaptive service placement, highlighting its potential for intelligent transportation and mobility applications. |
| title | AI-Driven Vehicle Condition Monitoring with Cell-Aware Edge Service Migration |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2506.02785 |