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Complex network theoretical analysis on information dissemination over vehicular networks

Wang, Jingjing, Jiang, Chunxiao, Gao, Longxiang, Yu, Shui, Han, Zhu and Ren, Yong 2016, Complex network theoretical analysis on information dissemination over vehicular networks, in ICC 2016. Preceedings of the IEEE International Conference on Communications, IEEE, Piscataway, N. J., pp. 1-6, doi: 10.1109/ICC.2016.7511133.

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Title Complex network theoretical analysis on information dissemination over vehicular networks
Author(s) Wang, Jingjing
Jiang, Chunxiao
Gao, LongxiangORCID iD for Gao, Longxiang orcid.org/0000-0002-3026-7537
Yu, ShuiORCID iD for Yu, Shui orcid.org/0000-0003-4485-6743
Han, Zhu
Ren, Yong
Conference name IEEE International Conference on Communications. Conference (2016 : KL, Malaysia)
Conference location Kuala Lumpur, Malaysia
Conference dates 22-27 May. 2016
Title of proceedings ICC 2016. Preceedings of the IEEE International Conference on Communications
Editor(s) [Unknown]
Publication date 2016
Conference series IEEE International Conference on Communications
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) vehicles
complex networks
impedance
global positioning system
optimization
public transportation
analytical models
Summary How to enhance the communication efficiency and quality on vehicular networks is one critical important issue. While with the larger and larger scale of vehicular networks in dense cities, the real-world datasets show that the vehicular networks essentially belong to the complex network model. Meanwhile, the extensive research on complex networks has shown that the complex network theory can both provide an accurate network illustration model and further make great contributions to the network design, optimization and management. In this paper, we start with analyzing characteristics of a taxi GPS dataset and then establishing the vehicular-to-infrastructure, vehicle-to-vehicle and the hybrid communication model, respectively. Moreover, we propose a clustering algorithm for station selection, a traffic allocation optimization model and an information source selection model based on the communication performances and complex network theory.
ISBN 9781479966646
Language eng
DOI 10.1109/ICC.2016.7511133
Field of Research 080109 Pattern Recognition and Data Mining
080503 Networking and Communications
100699 Computer Hardware not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30086216

Document type: Conference Paper
Collection: School of Information Technology
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