This book provides a holistic and comprehensive approach to deep learning for vehicular ad hoc networks (VANETs), covering various aspects such as applications, agency involvement, and potential ethical and legal issues. It begins with discussions on how the transportation system has been converted into Intelligent Transportation System (ITS). The use of VANETs is increasing in the development of ITS to enhance road safety, traffic efficiency, and driver comfort. However, the dynamic nature of vehicular environments and the high mobility of vehicles pose significant challenges to designing and implementing VANETs and ensuring reliable and efficient communication. Deep learning, a subset of machine learning, has the potential to revolutionize vehicular ad hoc networks (VANETs) to enable various applications such as traffic management, collision avoidance, and infotainment. DL has demonstrated great potential in addressing various challenges involved in VANETs by leveraging its ability to learn from vast data and make accurate predictions. It reviews the state-of-the-art DL-based approaches for various applications in VANETs, including routing, congestion control, autonomous driving, and security. In addition, this book provides a comprehensive analysis of these approaches' advantages and limitations and discusses their future research directions. The study in this book shows that DL-based techniques can significantly improve the performance and reliability of VANETs. Still, in-depth research is required to address the challenges of deploying these methods in real-world scenarios. Finally, the book discusses the potential of DL-based VANETs in supporting other emerging technologies, such as autonomous driving and smart cities. It explores the simulation/emulation tools for practical exposure to the vehicular ad hoc network


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep learning based solutions for vehicular adhoc networks


    Beteiligte:
    Bhatia, Jitendra (Herausgeber:in) / Tanwar, Sudeep (Herausgeber:in) / Rodrigues, Joel (Herausgeber:in) / Kumhar, Malaram (Herausgeber:in)

    Erscheinungsdatum :

    2025


    Format / Umfang :

    xv, 392 Seiten


    Anmerkungen:

    Illustrationen, Diagramme
    Literaturangaben



    Medientyp :

    Buch


    Format :

    Print


    Sprache :

    Englisch




    Vehicular Adhoc Networks: A Review

    Marwah, Gagan Preet Kour / Jain, Anuj | Springer Verlag | 2023


    A Study of Vehicular Adhoc Networks

    Rajput, Sakshi / Nidhi | IEEE | 2019


    Instantly Decodable RaptorQ Intersessions in Vehicular Adhoc Networks

    Vineeth Nandhini / Guruprasad H. S. | DOAJ | 2020

    Freier Zugriff

    Cluster Based Data Aggregation in Vehicular Adhoc Network

    Shoaib, Muhammad / Song, Wang-Cheol / Kim, Keun Hyung | Springer Verlag | 2012


    CARAVAN: a communications architecture for reliable adaptive vehicular adhoc networks

    Blum,J. / Eskandarian,A. / George Washington Univ.,US | Kraftfahrwesen | 2006