Background: Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems such as significant movement, overcrowding, and loss of data. This research presents a novel deep reinforcement learning-based resource allocation and congestion optimization (DRLRCO) framework aimed at improving communication efficiency and high reliability using an effective learning process. Method Used: The DRLRCO framework employs agent-based learning to ensure precise data transmission, AdMAC Protocol for dynamic packet delivery, and Congestion-Aware Chicken Swarm Optimization (CSO) to enhance network performance. Result: The DRLRCO framework surpasses reinforcement learning models for comprehensive testing. Significant enhancements involve improved accuracy, reduced data loss, minimized overhead, increased throughput, and shorter average delays for real-time communication. Conclusion: The DRLRCO framework successfully tackles congestion and data loss in vehicular communication. Future studies might involve Artificial Intelligence (AI) for traffic forecasting, experimentation among high-speed vehicles and investigating blockchain for enhanced security.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Reliable Multihop Routing and Congestion Aware Chicken Swarm Optimization in Vehicular Ad-Hoc Network


    Contributors:


    Publication date :

    2025-02-21


    Size :

    597632 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Congestion-Aware Routing in Software Defined Vehicular Networks

    Nikhita, M / Mohan, Rajasekar | IEEE | 2023


    Multi-Flow Congestion-Aware Routing in Software-Defined Vehicular Networks

    Di Maio, Antonio / Palattella, Maria Rita / Engel, Thomas | IEEE | 2019


    Reliable Location-Aware Routing Protocol for Urban Vehicular Scenario

    Srivastava, Ankita / Prakash, Arun | Springer Verlag | 2019



    A SECURITY AWARE FUZZY ENHANCED RELIABLE ANT COLONY OPTIMIZATION ROUTING IN VEHICULAR AD HOC NETWORKS

    Zhang, Hang / Bochem, Arne / Sun, Xu et al. | British Library Conference Proceedings | 2018