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Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou.
School of Information Engineering, Guangdong Mechanical & Electrical College, Guangzhou.
School of Information Science and Technology, Chengdu University.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0003-1902-9877
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2016 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, nr 1, artikkel-id 88Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
2016. Vol. 16, nr 1, artikkel-id 88
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URN: urn:nbn:se:ltu:diva-3645DOI: 10.3390/s16010088ISI: 000370679800089PubMedID: 26761013Scopus ID: 2-s2.0-84954308584Lokal ID: 1762a27f-6c12-47d3-94a1-c7753111efbdOAI: oai:DiVA.org:ltu-3645DiVA, id: diva2:976503
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Validerad; 2016; Nivå 2; 20160118 (andbra)Tilgjengelig fra: 2016-09-29 Laget: 2016-09-29 Sist oppdatert: 2018-07-10bibliografisk kontrollert

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