The importance of vehicle ad hoc networks (VANETs) lies in their ability to improve traffic monitoring, enhance road safety, and provide in-car infotainment. However, these networks face significant challenges, such as frequent disconnections between vehicles due to high mobility, limited bandwidth, roadside obstructions, and a scarcity of roadside units. Effective routing becomes a critical aspect in addressing these issues. In this paper, a novel approach utilizing a reinforcement learning (RL) strategy based on the Q-learning algorithm is presented. The objective is to use RL to enable vehicles to establish and maintain a stable connection even in the presence of multiple roadside units, thereby mitigating the problem of frequent disconnections. This innovative solution aims to enhance the overall performance of VANETs, & hence contributing to more seamless and efficient V2V2I communication among vehicles on the road.


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    Titel :

    V2V2I VANET Data Offloading Path Using Reinforcement Learning


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Tuba, Milan (Herausgeber:in) / Akashe, Shyam (Herausgeber:in) / Joshi, Amit (Herausgeber:in) / Raoof, Raneena (Autor:in) / Santhameena, S. (Autor:in)

    Kongress:

    International Conference on ICT for Sustainable Development ; 2024 ; Goa, India August 08, 2024 - August 09, 2024



    Erscheinungsdatum :

    03.05.2025


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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