Advertisement

Vehicular Crowdsensing for Smart Cities

  • Tzu-Yang Yu
  • Xiru Zhu
  • Muthucumaru MaheswaranEmail author
Chapter

Abstract

As smart vehicles begin to roam the streets, new possibilities will emerge for large-scale data acquisition tasks necessary for proactive smart cities applications. Unlike mobile devices, smart vehicles carry powerful sensors and are highly mobile; they can cover large areas and perform high quality sensing. However due to restricted reward structures and limited bandwidths of cellular and VANETs, not all vehicles can participate equally. Thus, we must find a method for selecting promising participants which can efficiently the required collect sensing information. In this chapter, we present ideas for participant selection under varying conditions from large scale crowdsensing to personalized crowdsensing. We present several algorithms using a common framework.

References

  1. 1.
    Cisco visual networking index: Global mobile data traffic forecast update, 2016–2021 white paper, Mar 2017.Google Scholar
  2. 2.
    S. Al-Sultan, M. M. Al-Doori, A. H. Al-Bayatti, and H. Zedan. A comprehensive survey on vehicular ad hoc network. Journal of network and computer applications, 37:380–392, 2014.CrossRefGoogle Scholar
  3. 3.
    L. Bracciale, M. Bonola, P. Loreti, G. Bianchi, R. Amici, and A. Rabuffi. CRAWDAD dataset roma/taxi (v. 2014-07-17). Downloaded from https://crawdad.org/roma/taxi/20140717, July 2014.
  4. 4.
    M. Buhrmester, T. Kwang, and S. D. Gosling. Amazon’s mechanical turk: A new source of inexpensive, yet high-quality, data? Perspectives on psychological science, 6(1):3–5, 2011.CrossRefGoogle Scholar
  5. 5.
    C. Cooper, D. Franklin, M. Ros, F. Safaei, and M. Abolhasan. A comparative survey of vanet clustering techniques. IEEE Communications Surveys & Tutorials, 19(1):657–681, 2017.CrossRefGoogle Scholar
  6. 6.
    Y. Gao, W. Dong, K. Guo, X. Liu, Y. Chen, X. Liu, J. Bu, and C. Chen. Mosaic: A low-cost mobile sensing system for urban air quality monitoring. In Computer Communications, IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on, pages 1–9. IEEE, 2016.Google Scholar
  7. 7.
    Google. Waze mobile, 2017. https://www.waze.com/.
  8. 8.
    S. A. Hamid, H. Abouzeid, H. S. Hassanein, and G. Takahara. Optimal recruitment of smart vehicles for reputation-aware public sensing. In Wireless Communications and Networking Conference (WCNC), 2014 IEEE, pages 3160–3165. IEEE, 2014.Google Scholar
  9. 9.
    K. Han, C. Chen, Q. Zhao, and X. Guan. Trajectory-based node selection scheme in vehicular crowdsensing. In Communications in China (ICCC), 2015 IEEE/CIC International Conference on, pages 1–6. IEEE, 2015.Google Scholar
  10. 10.
    Z. He, J. Cao, and X. Liu. High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility. In Computer Communications (INFOCOM), 2015 IEEE Conference on, pages 2542–2550. IEEE, 2015.Google Scholar
  11. 11.
    C. Hu, M. Xiao, L. Huang, and G. Gao. Truthful incentive mechanism for vehicle-based nondeterministic crowdsensing. In Quality of Service (IWQoS), 2016 IEEE/ACM 24th International Symposium on, pages 1–10. IEEE, 2016.Google Scholar
  12. 12.
    M. Hu, Z. Zhong, Y. Niu, and M. Ni. Duration-variable participant recruitment for urban crowdsourcing with indeterministic trajectories. IEEE Transactions on Vehicular Technology, 2017.Google Scholar
  13. 13.
    D. Krajzewicz, J. Erdmann, M. Behrisch, and L. Bieker. Recent development and applications of SUMO - Simulation of Urban MObility. International Journal On Advances in Systems and Measurements, 5(3&4):128–138, December 2012.Google Scholar
  14. 14.
    Y. Liu, J. Niu, and X. Liu. Comprehensive tempo-spatial data collection in crowd sensing using a heterogeneous sensing vehicle selection method. Personal and Ubiquitous Computing, 20(3):397–411, 2016.CrossRefGoogle Scholar
  15. 15.
    D. Peng, F. Wu, and G. Chen. Pay as how well you do: A quality based incentive mechanism for crowdsensing. In Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pages 177–186. ACM, 2015.Google Scholar
  16. 16.
    S. Reddy, D. Estrin, and M. Srivastava. Recruitment framework for participatory sensing data collections. In International Conference on Pervasive Computing, pages 138–155. Springer, 2010.Google Scholar
  17. 17.
    L. Shao, C. Wang, Z. Li, and C. Jiang. Traffic condition estimation using vehicular crowdsensing data. In 2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC), pages 1–8, Dec 2015.Google Scholar
  18. 18.
    S. Ucar, S. C. Ergen, and O. Ozkasap. Vmasc: Vehicular multi-hop algorithm for stable clustering in vehicular ad hoc networks. In Wireless Communications and Networking Conference (WCNC), 2013 IEEE, pages 2381–2386. IEEE, 2013.Google Scholar
  19. 19.
    S. Uppoor, O. Trullols-Cruces, M. Fiore, and J. M. Barcelo-Ordinas. Generation and analysis of a large-scale urban vehicular mobility dataset. IEEE Transactions on Mobile Computing, 13(5):1061–1075, 2014.CrossRefGoogle Scholar
  20. 20.
    M. Wu, D. Ye, S. Tang, and R. Yu. Collaborative vehicle sensing in bus networks: A stackelberg game approach. In Communications in China (ICCC), 2016 IEEE/CIC International Conference on, pages 1–6. IEEE, 2016.Google Scholar
  21. 21.
    K. Yi, R. Du, L. Liu, Q. Chen, and K. Gao. Fast participant recruitment algorithm for large-scale vehicle-based mobile crowd sensing. Pervasive and Mobile Computing, 2017.Google Scholar
  22. 22.
    T. Y. Yu, X. Zhu, and H. Chen. Gosense: Efficient vehicle selection for user defined vehicular crowdsensing. In 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), pages 1–5, June 2017.Google Scholar
  23. 23.
    X. Zhang, Z. Yang, W. Sun, Y. Liu, S. Tang, K. Xing, and X. Mao. Incentives for mobile crowd sensing: A survey. IEEE Communications Surveys & Tutorials, 18(1):54–67, 2016.CrossRefGoogle Scholar
  24. 24.
    X. Zhang, Z. Yang, Z. Zhou, H. Cai, L. Chen, and X. Li. Free market of crowdsourcing: Incentive mechanism design for mobile sensing. IEEE transactions on parallel and distributed systems, 25(12):3190–3200, 2014.CrossRefGoogle Scholar
  25. 25.
    D. Zhao, H. Ma, L. Liu, and X.-Y. Li. Opportunistic coverage for urban vehicular sensing. Computer Communications, 60:71–85, 2015.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.McGill UniversityMontrealCanada

Personalised recommendations