A novel vehicular information network architecture based on named data networking (NDN)

Zhiwei Yan, Sherali Zeadally, Yong Jin Park

    Research output: Contribution to journalArticle

    62 Citations (Scopus)

    Abstract

    Vehicular information network and Internet of Things (IoT) technologies have been receiving a lot of attention in recent years. As one of the most important and promising IoT areas, a vehicular information network aims to implement a myriad of applications related to vehicles, traffic information, drivers, passengers, and pedestrians. However, intervehicular communication (IVC) in a vehicular information network is still based on the TCP/IP protocol stack which is not efficient and scalable. To address the efficiency and scalability issues of the IVC, we leverage the named data networking (NDN) paradigm where the end user only cares about the needed content and pays no attention to the actual location of the content. The NDN model is highly suitable for the IVC scenario with its hierarchical content naming scheme and flexible content retrieval and caching support. We design a novel vehicular information network architecture based on the basic communication principle of NDN. Our proposed architecture aims to improve content naming, addressing, data aggregation, and mobility for IVC in the vehicular information network. In addition, the key parameter settings of the proposed schemes are analyzed in order to help guide their actual deployment.

    Original languageEnglish
    Article number6902767
    Pages (from-to)525-532
    Number of pages8
    JournalIEEE Internet of Things Journal
    Volume1
    Issue number6
    DOIs
    Publication statusPublished - 2014 Dec 1

    Keywords

    • Caching
    • Internet
    • Internet of Things (IoT)
    • mobility management
    • named data networking (NDN)
    • performance

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Computer Science Applications
    • Hardware and Architecture
    • Information Systems
    • Signal Processing
    • Information Systems and Management

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