The rapid advancement of vehicular technology and the proliferation of connected vehicles have given rise to the demand for efficient and responsive computing solutions within the vehicular environment. Vehicular Edge Computing (VEC) emerges as a promising paradigm to meet these demands by leveraging the computational resources at the network edge. This paper presents an in-depth exploration of Vehicular Edge Computing Architecture (VECA), a novel framework designed to enhance the capabilities of connected vehicles through edge computing. VECA integrates edge computing nodes, vehicle-to-everything (V2X) communication technologies, and intelligent algorithms to create a dynamic and distributed computing environment within the vehicular network. This architecture addresses critical challenges related to latency, bandwidth, and scalability, enabling a wide range of applications, including real-time navigation, autonomous driving, traffic management, and infotainment services. Key components of VECA include edge servers strategically placed at roadside infrastructure and within vehicles, a robust communication infrastructure that supports low-latency data exchange, and machine learning algorithms for predictive analytics and decision-making. The architecture fosters efficient resource allocation, load balancing, and secure data management, ensuring optimal utilization of computational resources while preserving data privacy. This paper provides a comprehensive overview of VECA’s architecture, highlighting its technical specifications, benefits, and potential use cases. This research paper also discusses the integration of the novel Device Based Edge-Cloud Architecture into existing vehicular networks, along with challenges and future research directions. Through the adoption of this new architecture, connected vehicles can harness the power of edge computing to enhance safety, efficiency, and user experience, ushering in a new era of intelligent and responsive vehicular systems.


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    Titel :

    A Novel Device Based Edge-Cloud Architecture for Vehicular Edge Computing


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Kumar, Amit (Herausgeber:in) / Gunjan, Vinit Kumar (Herausgeber:in) / Senatore, Sabrina (Herausgeber:in) / Hu, Yu-Chen (Herausgeber:in) / Raj, P. Herbert (Autor:in) / Kumar, P. Ravi (Autor:in) / Juwono, Filbert H. (Autor:in)

    Kongress:

    International Conference on Data Science, Machine Learning and Applications ; 2023 ; Hyderabad, India December 15, 2023 - December 16, 2023



    Erscheinungsdatum :

    20.10.2024


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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