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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T08:29:10Z","timestamp":1745396950383,"version":"3.37.3"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,1,11]],"date-time":"2016-01-11T00:00:00Z","timestamp":1452470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"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.<\/jats:p>","DOI":"10.3390\/s16010088","type":"journal-article","created":{"date-parts":[[2016,1,11]],"date-time":"2016-01-11T15:09:38Z","timestamp":1452524978000},"page":"88","source":"Crossref","is-referenced-by-count":206,"title":["Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9188-4179","authenticated-orcid":false,"given":"Jiafu","family":"Wan","sequence":"first","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"}]},{"given":"Jianqi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Guangdong Mechanical & Electrical College, Guangzhou 510515, China"}]},{"given":"Zehui","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Chengdu University, Chengdu 610106, China"}]},{"given":"Athanasios","family":"Vasilakos","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lule\u00e5 University of Technology, Lule\u00e5 97187, Sweden"}]},{"given":"Muhammad","family":"Imran","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia"}]},{"given":"Keliang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s10660-014-9147-2","article-title":"IoT Sensing Framework with Inter-cloud Computing Capability in Vehicular Networking","volume":"14","author":"Wan","year":"2014","journal-title":"Electron. 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