Reliable Content Dissemination in Internet of Vehicles Using Social Big Data

Zhenyu Zhou, Caixia Gao, Chen Xu, Yan Zhang, Di Zhang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    By analogy with internet of things (IoT), internet of vehicles (IoV) which enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information to realize rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks under various quality of service (QoS) requirements. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is derived by Bayesian nonparametric learning based on real-world social big data, which are collected from Sina Weibo and Youku. Then, a price-rising based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

    Original languageEnglish
    Title of host publication2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1-6
    Number of pages6
    Volume2018-January
    ISBN (Electronic)9781509050192
    DOIs
    Publication statusPublished - 2018 Jan 10
    Event2017 IEEE Global Communications Conference, GLOBECOM 2017 - Singapore, Singapore
    Duration: 2017 Dec 42017 Dec 8

    Other

    Other2017 IEEE Global Communications Conference, GLOBECOM 2017
    CountrySingapore
    CitySingapore
    Period17/12/417/12/8

    Fingerprint

    Internet
    Power control
    Big data
    Quality of service
    Industry

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Hardware and Architecture
    • Safety, Risk, Reliability and Quality

    Cite this

    Zhou, Z., Gao, C., Xu, C., Zhang, Y., & Zhang, D. (2018). Reliable Content Dissemination in Internet of Vehicles Using Social Big Data. In 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings (Vol. 2018-January, pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOCOM.2017.8254453

    Reliable Content Dissemination in Internet of Vehicles Using Social Big Data. / Zhou, Zhenyu; Gao, Caixia; Xu, Chen; Zhang, Yan; Zhang, Di.

    2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Zhou, Z, Gao, C, Xu, C, Zhang, Y & Zhang, D 2018, Reliable Content Dissemination in Internet of Vehicles Using Social Big Data. in 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2017 IEEE Global Communications Conference, GLOBECOM 2017, Singapore, Singapore, 17/12/4. https://doi.org/10.1109/GLOCOM.2017.8254453
    Zhou Z, Gao C, Xu C, Zhang Y, Zhang D. Reliable Content Dissemination in Internet of Vehicles Using Social Big Data. In 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-6 https://doi.org/10.1109/GLOCOM.2017.8254453
    Zhou, Zhenyu ; Gao, Caixia ; Xu, Chen ; Zhang, Yan ; Zhang, Di. / Reliable Content Dissemination in Internet of Vehicles Using Social Big Data. 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-6
    @inproceedings{59a3cc2a71fd4af0a686870fd217d95e,
    title = "Reliable Content Dissemination in Internet of Vehicles Using Social Big Data",
    abstract = "By analogy with internet of things (IoT), internet of vehicles (IoV) which enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information to realize rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks under various quality of service (QoS) requirements. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is derived by Bayesian nonparametric learning based on real-world social big data, which are collected from Sina Weibo and Youku. Then, a price-rising based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.",
    author = "Zhenyu Zhou and Caixia Gao and Chen Xu and Yan Zhang and Di Zhang",
    year = "2018",
    month = "1",
    day = "10",
    doi = "10.1109/GLOCOM.2017.8254453",
    language = "English",
    volume = "2018-January",
    pages = "1--6",
    booktitle = "2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",

    }

    TY - GEN

    T1 - Reliable Content Dissemination in Internet of Vehicles Using Social Big Data

    AU - Zhou, Zhenyu

    AU - Gao, Caixia

    AU - Xu, Chen

    AU - Zhang, Yan

    AU - Zhang, Di

    PY - 2018/1/10

    Y1 - 2018/1/10

    N2 - By analogy with internet of things (IoT), internet of vehicles (IoV) which enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information to realize rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks under various quality of service (QoS) requirements. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is derived by Bayesian nonparametric learning based on real-world social big data, which are collected from Sina Weibo and Youku. Then, a price-rising based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

    AB - By analogy with internet of things (IoT), internet of vehicles (IoV) which enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information to realize rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks under various quality of service (QoS) requirements. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is derived by Bayesian nonparametric learning based on real-world social big data, which are collected from Sina Weibo and Youku. Then, a price-rising based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

    UR - http://www.scopus.com/inward/record.url?scp=85046341459&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85046341459&partnerID=8YFLogxK

    U2 - 10.1109/GLOCOM.2017.8254453

    DO - 10.1109/GLOCOM.2017.8254453

    M3 - Conference contribution

    AN - SCOPUS:85046341459

    VL - 2018-January

    SP - 1

    EP - 6

    BT - 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings

    PB - Institute of Electrical and Electronics Engineers Inc.

    ER -