In order to mitigate the performance degradation of intermittent vehicular network caused by traffic mobility and sporadic connectivity issues, Vehicular Content Centric Network (VCCN) has been proposed to apply many technologies in Content Centric Network (CCN) into vehicular ad hoc networks. The open in-network caching strategy of CCN enables sharing and coordination of the cached data among multiple nodes as an efficient data access without relying on remote fetching. Nonetheless, few studies have considered the effective use of overall cache capacity with these cooperative nodes, especially the issues of cache duplication. Furthermore, most content replacement polices have ignored the needs of cooperative contents when making cache decisions. In this paper, we design a novel Cooperative Caching scheme by using Mobility Prediction and Consistent Hash for VCCN (called CCMPCH). Specifically, based on the observation that vehicles with the same trajectory are more likely to maintain stable communication links, we adopt Prediction by Partial Matching (PPM) to forecast each vehicle’s path and cluster the vehicles with similar future path, moving direction, and moving speed into one group. In each cluster, the consistent hash algorithm is used to allocate contents among cooperative nodes, thereby reducing unnecessary cache duplication while maintaining strong content availability. A popularity-based cache replacement policy is also developed to prioritize cooperative contents. We evaluate CCMPCH via extensive simulations, which demonstrates its higher cache hit ratio, shorter content access delay, and lower hop count compared to other state-of-the-art schemes.


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    Titel :

    A Cooperative Caching Scheme for VCCN With Mobility Prediction and Consistent Hashing


    Beteiligte:
    Yao, Lin (Autor:in) / Xu, Xiaoying (Autor:in) / Deng, Jing (Autor:in) / Wu, Guowei (Autor:in) / Li, Zhaoyang (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2022


    Format / Umfang :

    3011292 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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