The appropriate design of a vehicular ad hoc network (VANET) has become a pivotal way to build an efficient smart transportation system, which enables various applications associated with traffic safety and highly-efficient transportation. VANETs are vulnerable to the threat of malicious nodes stemming from its dynamicity and infrastructure-less nature and causing performance degradation. Recently, software-defined networking (SDN) has provided a feasible way to manage VANETs dynamically. In this article, we propose a novel software-defined trust based VANET architecture (SD-TDQL) in which the centralized SDN controller is served as a learning agent to get the optimal communication link policy using a deep $Q$ -learning approach. The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Specifically, we use the expected transmission count ( $ETX$ ) as a metric to evaluate the quality of the communication link for the connected vehicles’ communication. Moreover, we design a trust model to avoid the bad influence of malicious vehicles. Simulation results prove that the proposed SD-TDQL framework enhances the link quality.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Software-Defined Vehicular Networks With Trust Management: A Deep Reinforcement Learning Approach


    Contributors:
    Zhang, Dajun (author) / Yu, F. Richard (author) / Yang, Ruizhe (author) / Zhu, Li (author)


    Publication date :

    2022-02-01


    Size :

    2424481 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    5G Software Defined Vehicular Networks

    Ge, Xiaohu / Li, Zipeng / Li, Shikuan | ArXiv | 2017

    Free access

    Multihoming in Software Defined Vehicular Networks

    Ferreira, Nuno / Luis, Miguel / Rito, Pedro et al. | IEEE | 2024


    Deep Active Learning Intrusion Detection and Load Balancing in Software-Defined Vehicular Networks

    Ahmed, Usman / Lin, Jerry Chun-Wei / Srivastava, Gautam et al. | IEEE | 2023


    Data Dissemination in Software-Defined Vehicular Networks

    Ni, Yuanzhi / He, Jianping / Cai, Lin | IEEE | 2017


    Congestion-Aware Routing in Software Defined Vehicular Networks

    Nikhita, M / Mohan, Rajasekar | IEEE | 2023