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
N1 - Funding Information:
ACKNOWLEDGMENT This work was partially supported by the National Science Foundation of China (NSFC) under Grant 61601181, the Fundamental Research Funds for the Central Universities under Grant 2017MS13, by the Beijing Natural Science Foundation under Grant 4174104, and Beijing Outstanding Young Talent under Grant 2016000020124G081. This work was also partially supported by the projects 240079/F20 funded by the Research Council of Norway.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
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.
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U2 - 10.1109/GLOCOM.2017.8254453
DO - 10.1109/GLOCOM.2017.8254453
M3 - Conference contribution
AN - SCOPUS:85046341459
T3 - 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
SP - 1
EP - 6
BT - 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Global Communications Conference, GLOBECOM 2017
Y2 - 4 December 2017 through 8 December 2017
ER -