A Study on Vehicular Content DeliveryAuthor : Subramanyam Kunisetti and Suban Ravichandran
Volume 7 No.1 Special Issue:November 2018 pp 38-45
The presence of the net of Vehicles licenses comforts driving encounters and substance rich sight and sound framework administrations for in-vehicle clients. The movement arranges gives particular situation centrically content conveyance administrations including data of auto standing, client practices, and ecological choices. In this paper, we tend to target transport content conveyance from a gigantic in view of a data point. At the point when an exhaustive survey of dynamic works, we tend to expand the potential cost of huge data in vehicular information and substance benefits by presenting numerous regular application circumstances. Per the data qualities, we tend to group the transport data into 3 classes, that is, an area is driven, client-driven, and vehicle-driven, and afterward represent relate execution of huge data arrangement and investigation. A true enormous data application in social-based transport systems is given, and reenactment results demonstrate that the tremendous data empowered substance conveyance technique will get an execution gain of client fulfillment with the conveyed substance compared to the case rudely of social huge information. At last, we tend to close the article with disjoining al future examination subjects.
Vehicular Content, Vehicle-To-Vehicle, QoE, Big Data
 Chen Xu, Zhenyu Zhou, “Vehicular Content Delivery: A Big Data Perspective”, IEEE Wireless Communications, 2018.
 P. Popovski et al., “Scenarios, Requirements and KPIs for 5G Mobile and Wireless System”,ICT-317669-METIS/D1.1, Apr. 2013.
 A. Vegni et al., “A Survey on Vehicular Social Networks”, IEEE Commun. Surveys and Tutorials, Vol. 17, No. 4, pp. 2397–2419, July 2015.
 R. Yu et al., “Cooperative Resource Management in Cloud-Enabled Vehicular Networks”, IEEE Trans. Ind. Electron., Vol. 62, No. 12, pp. 7938–51, Dec. 2015.
 M. Amadeo et al., “Information-Centric Networking for Connected Vehicles: A Survey and Future Perspectives”, IEEE Commun. Mag., Vol. 54, No. 2, pp. 98–104, Feb. 2016.
 Z. Su et al., “D2D-Based Content Delivery with Parked Vehicles in Vehicular Social Networks”, IEEE Wireless Commun., Vol. 23, No. 4, pp. 90–95, Aug. 2016.
 C. Xu et al., “Social Network-Based Content Delivery in Device-to-Device Underlay Cellular Networks Using Matching Theory”, IEEE Access, Vol. 5, pp. 924–37, Nov. 2016.
 K. Wang et al., “Wireless Big Data Computing in Smart Grid”, IEEE Wireless Communication, Vol. 24, No. 2,58–64, Apr. 2017.
 Y. Zhang et al., “Social Vehicle Swarms: A Novel Perspective on Socially Aware Vehicular Communication Architecture”, IEEE Wireless Communion, Vol. 23, No. 4,82–89, Aug. 2016.
 H. Li et al., “VeShare: A D2D Infrastructure for Real-Time Social-Enabled Vehicle Networks”, IEEE Wireless Commun, Vol. 23, No. 4, pp. 96–102, Aug. 2016.
 M. Zhang et al., “SafeDrive: Online Driving Anomaly Detection from Large-Scale Vehicle Data”, IEEE Trans. Ind. Informat., Vol. 13, No. 4, pp. 2087–96.
 K. Lin et al., “Localization Based on Social Big Data Analysis in the Vehicular Networks”, IEEE Trans. Ind. Information, Vol. 13, No. 4, pp. 1932–40.
 M. Burt et al., “Big Data’s Implications for Transportation Operations: An Exploration”, [Online] Available at: http://www.its.dot. gov/index.html,Dec. 2014
 S. Bi et al., “Wireless Communications in the Era of Big Data”, IEEE Commun. Mag., Vol. 53, No. 10, pp. 190–99, Oct. 2015.
 T. H. Luan et al., “Feel Bored? Join Verse! Engineering Vehicular Proximity Social Networks”, IEEE Trans. Vehic. Tech., Vol. 64, No. 3, pp. 1120–31, Mar. 2015.