AI-Driven Vehicle Condition Monitoring with Cell-Aware Edge Service Migration

• URL: http://arxiv.org/abs/2506.02785v1
• Date: Tue, 03 Jun 2025 12:12:27 GMT
• Title: AI-Driven Vehicle Condition Monitoring with Cell-Aware Edge Service Migration
• Authors: Charalampos Kalalas, Pavol Mulinka, Guillermo Candela Belmonte, Miguel Fornell, Michail Dalgitsis, Francisco Paredes Vera, Javier Santaella Sánchez, Carmen Vicente Villares, Roshan Sedar, Eftychia Datsika, Angelos Antonopoulos, Antonio Fernández Ojea, Miquel Payaro,
• Abstract summary: We introduce a novel vehicle condition monitoring service that enables real-time diagnostics of a diverse set of anomalies.<n>We propose a closed-loop service orchestration framework where service migration across edge nodes is dynamically triggered by network-related metrics.<n>Our approach has been implemented and tested in a real-world race circuit environment equipped with 5G network capabilities.
• Score: 1.499644443137431
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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