- Title
- Mobile crowd sensing for traffic prediction in internet of vehicles
- Creator
- Wan, Jiafu; Liu, Jianqi; Shao, Zehui; Vasilakos, Athanasios; Imran, Muhammad; Zhou, Keliang
- Date
- 2016
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186385
- Identifier
- vital:16899
- Identifier
-
https://doi.org/10.3390/s16010088
- Identifier
- ISBN:1424-8220 (ISSN)
- Abstract
- The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction. © 2016 by the authors, licensee MDPI, Basel, Switzerland.
- Publisher
- MDPI AG
- Relation
- Sensors (Switzerland) Vol. 16, no. 1 (2016), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2016 by the authors
- Rights
- Open Access
- Subject
- 4008 Electrical engineering; 4009 Electronics, sensors and digital hardware; 4606 Distributed computing and systems softwareCloud computing; Data aggregation; Internet of vehicles; Mobile crowd sensing; Traffic prediction
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