Search results for "C-V2X"
showing 2 items of 2 documents
Edge Computing-enabled Intrusion Detection for C-V2X Networks using Federated Learning
2022
Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using machine learning (ML) techniques. However, it has been shown that generating ML-based models in a centralized way consumes a massive quantity of network resources, such as CPU/memory and bandwidth, which may represent a critical issue in such networks. To avoid this problem, the new concept of Federated Learning (FL) emerged to build ML-based models in a distributed and collaborative way. In such an approach, the set of nodes, e.g., vehicles or gNodeB, collaborate to create a global ML model trained across thes…
Collecte des données Véhicule/Environnement et remontée avec réseau Cellulaire et réseau Véhiculaire
2019
Vertical handover is one of the key technologies that will facilitate the connected and autonomous vehicles deployment. Today, the emergence of Vehicular Ad hoc Networks (VANETs): Vehicle-to-Vehicle (V2V) communications, Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) has enabled new applications such as Cooperative Intelligent Transport Systems (C-ITS), real-time applications (for example, autonomous driving), road traffic management applications and comfort applications. However, these networks are characterized by a high level of mobility and dynamic change in the topology, which generates scattered networks and requires handover mechanisms for maintaining ongoing session…