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Open AccessArticle

Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles

School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
School of Information Engineering, Guangdong Mechanical & Electrical College, Guangzhou 510515, China
School of Information Science and Technology, Chengdu University, Chengdu 610106, China
Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå 97187, Sweden
College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China
Author to whom correspondence should be addressed.
Academic Editors: Neal N. Xiong and Xuefeng Liang
Sensors 2016, 16(1), 88;
Received: 9 October 2015 / Revised: 28 December 2015 / Accepted: 28 December 2015 / Published: 11 January 2016
(This article belongs to the Special Issue Mobile Sensor Computing: Theory and Applications)
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. View Full-Text
Keywords: mobile crowd sensing; traffic prediction; internet of vehicles; data aggregation; cloud computing mobile crowd sensing; traffic prediction; internet of vehicles; data aggregation; cloud computing
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MDPI and ACS Style

Wan, J.; Liu, J.; Shao, Z.; Vasilakos, A.V.; Imran, M.; Zhou, K. Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles. Sensors 2016, 16, 88.

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