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VCLT: An Accurate Trajectory Tracking Attack Based on Crowdsourcing in VANETs

  • Chi Lin
  • Kun Liu
  • Bo Xu
  • Jing Deng
  • Chang Wu Yu
  • Guowei Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9530)

Abstract

We investigate trajectory tracking in Vehicular Ad hoc Networks (VANETs) in this work. Previous tracking methods suffer from low accuracy, large overhead, and big error. In this paper, we propose a Vehicular Crowdsourcing Localization and Tracking (VCLT) scheme for mounting a trajectory tracking attack. In our scheme, crowdsourcing technique is applied to sample the location information of certain users. Then matrix completion algorithm is used to generate our predictions of the users’ trajectories. To alleviate the error disturbance of the recovered location data, Kalman filter technique is implemented and the trajectories of certain users are recovered with accuracy. At last, extensive simulations are conducted to show the performance of our scheme. Simulations results reveal that the proposed approach is able to accurately track the trajectories of certain users.

Keywords

Trajectory tracking Crowdsourcing Matrix completion Kalman filter VANETs 

Notes

Acknowledgments

This research is sponsored in part by the National Natural Science Foundation of China (No.61173179, No.61402078 and No.61502071). This research is also sponsored in part supported by the Fundamental Research Funds for the Central Universities (DUT14RC(3)106, No.DUT14RC(3)090).

References

  1. 1.
    Zeadally, S., Hunt, R., Chen, Y., et al.: Vehicular ad hoc networks (VANETS): status, results, and challenges. Telecommun. Syst. 50(4), 217–241 (2012)CrossRefGoogle Scholar
  2. 2.
    Reza, T.A., Barbeau, M., Lamothe, G., et al.: Non-cooperating vehicle tracking in VANETs using the conditional logit model. In: International IEEE Conference on Intelligent Transportation Systems, pp. 626–633. IEEE (2013)Google Scholar
  3. 3.
    Hao, P., Boriboonsomsin, K., Wu, G., et al.: Probabilistic model for estimating vehicle trajectories using sparse mobile sensor data. In: IEEE International Conference on Intelligent Transportation Systems, pp. 1363–1368. IEEE (2014)Google Scholar
  4. 4.
    Xue, G., Luo, Y., Yu, J., et al.: A novel vehicular location prediction based on mobility patterns for routing in urban VANET. EURASIP J. Wireless Commun. Networking 2012(1), 1–14 (2012)CrossRefGoogle Scholar
  5. 5.
    Shafiee, K., Leung, V.C.M.: Connectivity-aware minimum-delay geographic routing with vehicle tracking in VANETs. Ad Hoc Netw. 9(2), 131–141 (2011)CrossRefGoogle Scholar
  6. 6.
    Tsai, M., Wang, P., Shieh, C., et al.: Improving positioning accuracy for VANET in real city environments. J. Supercomput., 1–21 (2014)Google Scholar
  7. 7.
    Thangavelul, A., Bhuvaneswari, K., Kumar, K., et al.: Location Identification and Vehicle Tracking using VANET (VETRAC) (2007)Google Scholar
  8. 8.
    Cheng, L., Henty, B.E., Stancil, D.D., et al.: Mobile vehicle-to-vehicle narrow-band channel measurement and characterization of the 5.9 GHz dedicated short range communication (DSRC) frequency band. IEEE J. Sel. Areas Commun. 25(8), 1501–1516 (2007)CrossRefGoogle Scholar
  9. 9.
    Jiang, D., Delgrossi, L.: IEEE 802.11p: Towards an international standard for wireless access in vehicular environments. In: IEEE Vehicular Technol Conference, pp. 2036–2040 (2008)Google Scholar
  10. 10.
    Vasquez, D., Fraichard, T., Laugier, C.: Growing hidden markov models: an incremental tool for learning and predicting human and vehicle motion. Int. J. Robot. Res. 28, 1486–1506 (2009)CrossRefGoogle Scholar
  11. 11.
    Fallah, C.H., Sengupta, Y.P., Krishnan, R., et al.: Adaptive intervehicle communication control for cooperative safety systems. Netw. IEEE 24(1), 6–13 (2010)CrossRefGoogle Scholar
  12. 12.
    Rezaei, S., Sengupta, R., Krishnan, H., et al.: Tracking the position of neighboring vehicles using wireless communications. Transp. Res. Part C Emerg. Technol. 18(3), 335–350 (2010)CrossRefGoogle Scholar
  13. 13.
    Jiang, Z., Zhao, J., Li, X., et al.: Communicating is crowdsourcing: Wi-Fi indoor localization with CSI-based speed estimation. J. Comput. Sci. Technol. 29(4), 589–604 (2014)CrossRefGoogle Scholar
  14. 14.
    Artikis, A., Weidlich, M.: Heterogeneous stream processing and crowdsourcing for urban traffic management. In: Edbt (2014)Google Scholar
  15. 15.
    Suriyapaibonwattana, K., Pomavalai, C.: An effective safety alert broadcast algorithm for VANET. In: International Symposium on Communications & Information Technologies, pp. 247–250. IEEE (2008)Google Scholar
  16. 16.
    Corporation, H.P.: Mobility crowdsourcing: toward zero-effort carpooling on individual smartphone. Int. J. Distrib. Sens. Netw. 12(3), 188–192 (2013)Google Scholar
  17. 17.
    Cands, E.J., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717–772 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Wang, J., Tang, S., Yin, B., et al.: Data gathering in wireless sensor networks through intelligent compressive sensing. In: Infocom, IEEE, pp. 603–611. IEEE (2012)Google Scholar
  19. 19.
    Wang, H., Zhu, Y., Zhang, Q.: Compressive sensing based monitoring with vehicular networks. In: Infocom, IEEE, pp. 2823–2831. IEEE (2013)Google Scholar
  20. 20.
    Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Eprint Arxiv (2010)Google Scholar
  21. 21.
    Welch, G., Bishop, G.: An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill (1995)Google Scholar
  22. 22.
  23. 23.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Chi Lin
    • 1
    • 2
  • Kun Liu
    • 1
    • 2
  • Bo Xu
    • 1
    • 2
  • Jing Deng
    • 3
  • Chang Wu Yu
    • 4
  • Guowei Wu
    • 1
    • 2
  1. 1.School of SoftwareDalian University of TechnologyDalianChina
  2. 2.Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalianChina
  3. 3.Department of Computer ScienceUniversity of North Carolina at GreensboroGreensboroUSA
  4. 4.Department of Computer Science and Information EngineeringChung Hua UniversityHsinchuTaiwan

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