Secure and Efficient Content Distribution in Crowdsourced Vehicular Content-Centric Networking

Chengming Li, Shimin Gong, Xiaojie Wang, Lei Wang, Qingshan Jiang, Koji Okamura

    Research output: Contribution to journalArticlepeer-review

    18 Citations (Scopus)


    In future intelligent transportation systems, a large amount of content needs to be efficiently and securely exchanged between vehicles and roadside units via vehicular networks to improve the driving and traveling experience. To solve the challenges caused by poor-quality wireless links and the mobility of vehicles, vehicular content-centric networking (VCCN) emerges as a promising paradigm, which has a better content distribution efficiency, mobility, and security via named data and in-networking caching compared with an IP-based network. However, providing a high-quality experience for content distribution in VCCN is challenging due to the dynamic network topologies, varying wireless channel conditions, and vehicle user privacy. In this paper, we propose a novel crowdsourced VCCN framework for secure and efficient content distribution. This framework enables the nearby vehicles to crowdsource their caching resources and radio links for cooperative content distribution. We formulate the problem as the maximization of all users' payoff and propose an online scheduling method to solve this solution. Furthermore, we adopt identity-based proxy reencryption and named function networking to secure the process of content distribution. The simulation results show that our proposals improve the performance of VCCN in terms of average requester utility compared with original CCN forwarding strategies.

    Original languageEnglish
    Pages (from-to)5727-5739
    Number of pages13
    JournalIEEE Access
    Publication statusPublished - Nov 28 2017

    All Science Journal Classification (ASJC) codes

    • Computer Science(all)
    • Materials Science(all)
    • Engineering(all)


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