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Mobile Networks and Applications

, Volume 22, Issue 6, pp 1212–1218 | Cite as

Cloud-Assisted Mobile Crowd Sensing for Traffic Congestion Control

  • Hehua Yan
  • Qingsong Hua
  • Daqiang Zhang
  • Jiafu Wan
  • Seungmin Rho
  • Houbing Song
Article

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.

Keywords

Mobile crowd sensing Urban transportation Cloud computing 

Notes

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).

References

  1. 1.
    Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150CrossRefGoogle Scholar
  2. 2.
    Yuan W, Deng P, Taleb T, Wan J, Bi C (2016) An unlicensed taxi identification model based on big data analysis. IEEE Trans Intell Transp Syst 17(6):1703–1713CrossRefGoogle Scholar
  3. 3.
    Liu J, Wan J, Wang Q, Li D, Qiao Y, Cai H (2015) A novel energy-saving one-sided synchronous two-way ranging algorithm for vehicular positioning. Mobile Networks and Applications 20(5):661–672CrossRefGoogle Scholar
  4. 4.
    Wan J, Zhang D, Sun Y, Lin K, Zou C, Cai H (2014) VCMIA: a novel architecture for integrating vehicular cyber-physical systems and mobile cloud computing. Mobile Networks and Applications 19(2):153–160CrossRefGoogle Scholar
  5. 5.
    Chen M, Leung V, Mao S, Xiao Y, Chlamtac I (2009) Hybrid geographical routing for flexible energy-delay tradeoffs. IEEE Trans Veh Technol 58(9):4976–4988CrossRefGoogle Scholar
  6. 6.
    Chen M, Mau D, Zhang Y, Taleb T, Leung V (2014) VENDNET: vehicular named data network. Vehicular Communications 1(4):208–213CrossRefGoogle Scholar
  7. 7.
    Liu J, Wan J, Wang Q, Deng P, Zhou K, Qiao Y (2016) A survey on position-based routing for vehicular ad hoc networks. Telecommun Syst 62(1):15–30CrossRefGoogle Scholar
  8. 8.
    D. Zhang, J. Wan, Z. He, S. Zhao, K. Fan, S. Park and Z. Jiang, “Identifying Region-wide Functions Using Urban Taxicab Trajectories,” ACM Transactions on Embedded Computing Systems, vol. 15, no. 2, Article 36, 2016. doi: 10.1145/2821507.
  9. 9.
    Hull B, Bychkovsky V, Zhang Y, Chen K, Goraczko M, Miu A, Shih E, Balakrishnan H, and Madden S (2006) Cartel: a distributed mobile sensor computing system. In Proceedings of the 4th international conference on Embedded networked sensor systems. ACM, 125-138.Google Scholar
  10. 10.
    Mohan P, Padmanabhan VN, and Ramjee R (2008) Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In Proceedings of the 6th ACM conference on Embedded network sensor systems. ACM, 323-336.Google Scholar
  11. 11.
    Herrera JC, Work DB, Herring R, Ban XJ, Jacobson Q, Bayen AM (2010) Evaluation of traffic data obtained via gps-enabled mobile phones: the mobile century field experiment. Transportation Research Part C: Emerging Technologies 18(4):568–583CrossRefGoogle Scholar
  12. 12.
    Mathur S, Jin T, Kasturirangan N, Chandrasekaran J, Xue W, Gruteser M, and Trappe W (2010) Parknet: driveby sensing of road-side parking statistics. In Proceedings of the 8th international conference on Mobile systems, applications, and services. ACM, 123-136.Google Scholar
  13. 13.
    Ali K, Al-Yaseen D, Ejaz A, Javed T, and Hassanein HS (2012) Crowdits: crowdsourcing in intelligent transportation systems. In Wireless Communications and Networking Conference (WCNC), 2012 IEEE. IEEE, 3307-3311.Google Scholar
  14. 14.
    Liu J, Wan J, Jia D, Zeng B, Li D, Hsu C, Chen H (2017) High-efficiency Urban-Traffic Management in context-aware computing and 5G communication. IEEE Commun Mag 55(1):34–40CrossRefGoogle Scholar
  15. 15.
    Shu Z, Wan J, Zhang D, Li D (2016) Cloud-integrated cyber-physical Systems for Complex Industrial Applications. Mobile Networks and Applications 21(5):865–878CrossRefGoogle Scholar
  16. 16.
    Yuan W, Deng P, Yang C, Wan J, Zhang D, Chen X, Bi C, Li Y (2015) A smart work performance measurement system for police officers. IEEE Access 3:1755–1764CrossRefGoogle Scholar
  17. 17.
    Wan J, Zou C, Zhou K, Lu R, Li D (2014) IoT sensing framework with inter-cloud computing capability in vehicular networking. Electron Commer Res 3:1755–1764Google Scholar
  18. 18.
    Wan J, Liu J, Shao Z, Vasilakos A, Imran M and Zhou K (2016) Mobile crowd sensing for traffic prediction in internet of vehicles. Sensors 16(1):88. doi: 10.3390/s16010088

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Hehua Yan
    • 1
  • Qingsong Hua
    • 2
  • Daqiang Zhang
    • 3
  • Jiafu Wan
    • 4
  • Seungmin Rho
    • 5
  • Houbing Song
    • 6
  1. 1.Guangdong Mechanical and Electrical CollegeGuangzhouChina
  2. 2.School of Mechanical and Electrical EngineeringQingdao UniversityQingdaoChina
  3. 3.School of Software EngineeringTongji UniversityShanghaiChina
  4. 4.Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing TechnologySouth China University of TechnologyGuangzhouChina
  5. 5.Department of MultimediaSungkyul UniversityAnyangSouth Korea
  6. 6.Department of Electrical and Computer EngineeringWest Virginia UniversityMorgantownUSA

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