Demand for smart driving in cars spurs development of time-sensitive networks in vehicles. Traffic scheduling mechanism is the core mechanism of time-sensitive networks, which realize latency and bandwidth requirements for different types of traffic flows by scheduling the order and time of data frames in the network. To address the problems of poor performance and easy to fall into local optimal solutions of existing genetic traffic scheduling algorithms, we propose an algorithm combining variable neighborhood search and population perturbation in genetic algorithm, which takes the queuing delay generated during traffic scheduling as an adaptive function, and improves the adaptation of individuals through iteration of population to obtain the optimal data frame transmission for traffic scheduling The optimal data frame transmission order for traffic scheduling is obtained by iterating the population to improve individual adaptation. The algorithm is demonstrated to have better performance through simulation comparison experiments.


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    Titel :

    Hybrid Genetic-Based Traffic Scheduling Algorithm in In-Vehicle Time Sensitive Networks


    Beteiligte:
    Zhu, Xiaosong (Autor:in) / Gong, Bei (Autor:in) / Zhu, Haotian (Autor:in)


    Erscheinungsdatum :

    01.05.2023


    Format / Umfang :

    322763 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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