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

TL;DR

This paper presents an AI-driven vehicle condition monitoring system that uses edge computing and dynamic service migration to enable real-time diagnostics and anomaly detection in vehicular environments, tested in a real-world 5G-enabled race circuit.

Contribution

It introduces a novel closed-loop service orchestration framework for dynamic AI service migration across edge nodes in vehicular environments, addressing mobility and latency challenges.

Findings

Effective real-time anomaly detection in vehicular data

Low-latency AI inference achieved in edge environments

Adaptive service placement improves operational performance

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.