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Communication Dans Un Congrès Année : 2023

Predictive Network Configuration with Hierarchical Spectral Clustering for Software Defined Vehicles

Configuration Réseau Prédictive avec Partitionnement Spectral Hiérarchique pour les Véhicules Définis par Logiciel

Résumé

The increasing connectivity and autonomy of vehicles has led to a growing need for dynamic and real-time adjustments to software and network configurations. Software Defined Vehicles (SDV) have emerged as a potential solution to adapt to changing user needs with continuous updates and onboard reconfigurations to offer infotainment, connected, and background services such as cooperative driving. However, network configuration management in SDVs remains a significant challenge, particularly in the context of shared Ethernet-based in-vehicle networks. Traditional worst-case static configuration methods cannot efficiently allocate network resources while ensuring Quality of Service (QoS) guarantees for each network flow within the physical topology capabilities. In this work, we propose a configuration generation methodology that addresses these limitations by dynamically switching between pre-computed offboard configurations downloaded to the vehicle. Simulation results are presented and future work is discussed.
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Licence : CC BY NC - Paternité - Pas d'utilisation commerciale

Dates et versions

hal-04186418 , version 1 (23-08-2023)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Pierre Laclau, Stéphane Bonnet, Bertrand Ducourthial, Xiaoting Li, Trista Lin. Predictive Network Configuration with Hierarchical Spectral Clustering for Software Defined Vehicles. 2023 IEEE 97th Vehicular Technology Conference (VTC 2023), Vehicular Technology Society, Jun 2023, Florence, Italy. pp.1-5, ⟨10.1109/VTC2023-Spring57618.2023.10199920⟩. ⟨hal-04186418⟩
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