… the physical safety of vehicle and passengers. In this paper, a novel machine learning
based approach is used to devise an Intrusion Detection System for the CAN bus network. The …

… advancements in intelligent vehicular networks. The … of machine learning is that when
vehicles learn and improve to operate by the previously processed data. The machine learning …

… vehicular networks, different solutions such as non-orthogonal multiple access, device-to-device
(D2D) communications, machine learning, blockchain, wireless social networking and …

… In this paper, we utilize federated learning and knowledge distillation to save communication
resources in a vehicular network. In order to reduce the communication overhead, we …

… Therefore, futuristic technologies such as machine learning and Software-Defined Network
(SDN) are used in the clustering process to improve cluster formation and maintenance …

… We start with the traditional machine learning (TML) algorithms because these state-of-the-art
algorithms provide the optimal performance and take the least amount of time to run. The …

… range of 5.85–5.925 GHz for vehicular networks; however, this … CR-VANET compared with
other wireless networks such as … in the vehicular network include vehicle-to-barrier (V2B) and …

… Our method elevates vehicular network performance by … Lets assume a vehicular network
with K randomly distributed … using AP machine learning algorithm. To be specific, using …

… More specifically, we bring the focus to the machine learning solutions and demonstrate …
Federated Machine Learning based platform. The platform’s major goal is to train the vehicle …

… problem in high mobility vehicular networks, where multiple V2V … To support diverse service
requirements in vehicular networks, … Wu, “Machine learning for vehicular networks: Recent …