Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/1/88 |
| _version_ | 1798043650584739840 |
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| author | Jiafu Wan Jianqi Liu Zehui Shao Athanasios V. Vasilakos Muhammad Imran Keliang Zhou |
| author_facet | Jiafu Wan Jianqi Liu Zehui Shao Athanasios V. Vasilakos Muhammad Imran Keliang Zhou |
| author_sort | Jiafu Wan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:52:02Z |
| format | Article |
| id | doaj.art-ab297e7a9013415a842d6db31037b305 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:52:02Z |
| publishDate | 2016-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ab297e7a9013415a842d6db31037b3052022-12-22T03:58:34ZengMDPI AGSensors1424-82202016-01-011618810.3390/s16010088s16010088Mobile Crowd Sensing for Traffic Prediction in Internet of VehiclesJiafu Wan0Jianqi Liu1Zehui Shao2Athanasios V. Vasilakos3Muhammad Imran4Keliang Zhou5School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, ChinaSchool of Information Engineering, Guangdong Mechanical & Electrical College, Guangzhou 510515, ChinaSchool of Information Science and Technology, Chengdu University, Chengdu 610106, ChinaDepartment of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå 97187, SwedenCollege of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaThe 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.http://www.mdpi.com/1424-8220/16/1/88mobile crowd sensingtraffic predictioninternet of vehiclesdata aggregationcloud computing |
| spellingShingle | Jiafu Wan Jianqi Liu Zehui Shao Athanasios V. Vasilakos Muhammad Imran Keliang Zhou Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles Sensors mobile crowd sensing traffic prediction internet of vehicles data aggregation cloud computing |
| title | Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles |
| title_full | Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles |
| title_fullStr | Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles |
| title_full_unstemmed | Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles |
| title_short | Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles |
| title_sort | mobile crowd sensing for traffic prediction in internet of vehicles |
| topic | mobile crowd sensing traffic prediction internet of vehicles data aggregation cloud computing |
| url | http://www.mdpi.com/1424-8220/16/1/88 |
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