In a vehicular ad-hoc network (VANET), vehicles can communicate with each other to exchange critical infor-mation. However, communications among vehicles may suffer from unstable transmission paths or high messaging overhead owing to the high mobility of vehicles. Vehicle clustering is an effective approach for VANET management. This paper presents a distributed clustering algorithm based on federated deep reinforcement learning. The proposed algorithm can improve the stability of vehicle-to-vehicle connections in a rapidly changing environment. The results show that our algorithm can improve the stability of clustering by reducing the number of role and cluster changes for vehicles.


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    Title :

    Stable Clustering in VANET Using Federated Deep Reinforcement Learning


    Contributors:


    Publication date :

    2023-07-23


    Size :

    856739 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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