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Cloud-Assisted Mobile Crowd Sensing for Traffic Congestion Control

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Abstract

With the development of mobile cloud computing, wireless communication techniques, intelligent mobile terminals, and data mining techniques, Mobile Crowd Sensing (MCS) as a new paradigm of the Internet of Things can be used in traffic congestion control to provide more convenient services and alleviate the traffic problems. In this paper, we propose a cloud-assisted MCS architecture for urban transportation system. Then, we make the case for cloud-assisted MCS traffic congestion control by sensing data obtained continuously from a large set of smartphones carried by drivers. In this case, we consider a Mechanism of more Contributions and more Feedback Services (MCFS) to recruit, engage, and retain the participants. Finally, we pay close attention to the issues and challenges, including system architecture, resource limitations, and security and privacy.

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Acknowledgements

This work was supported by the Natural Science Foundation of Guangdong Province (Nos. 2016A030313734, 2015A030313746, and 2016A030313735), the Research Fund Program of Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing (CIMSOF2016004), the Fundamental Research Funds for the Central Universities (No. 2015ZZ079), the Major Projects for Numerical Control Machine (2015ZX04005001), and the National Natural Science Foundation of China (No. 61572220).

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Authors and Affiliations

  1. Guangdong Mechanical and Electrical College, Guangzhou, China

    Hehua Yan

  2. School of Mechanical and Electrical Engineering, Qingdao University, Qingdao, China

    Qingsong Hua

  3. School of Software Engineering, Tongji University, Shanghai, China

    Daqiang Zhang

  4. Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, China

    Jiafu Wan

  5. Department of Multimedia, Sungkyul University, Anyang, South Korea

    Seungmin Rho

  6. Department of Electrical and Computer Engineering, West Virginia University, Morgantown, USA

    Houbing Song

Authors
  1. Hehua Yan
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  2. Qingsong Hua
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  3. Daqiang Zhang
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  4. Jiafu Wan
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  5. Seungmin Rho
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  6. Houbing Song
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Correspondence to Jiafu Wan.

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Yan, H., Hua, Q., Zhang, D. et al. Cloud-Assisted Mobile Crowd Sensing for Traffic Congestion Control. Mobile Netw Appl 22, 1212–1218 (2017). https://doi.org/10.1007/s11036-017-0873-2

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  • DOI: https://doi.org/10.1007/s11036-017-0873-2

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