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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zeadally, S., Hunt, R., Chen, Y., et al.: Vehicular ad hoc networks (VANETS): status, results, and challenges. Telecommun. Syst. 50(4), 217–241 (2012)
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)
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)
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)
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)
Tsai, M., Wang, P., Shieh, C., et al.: Improving positioning accuracy for VANET in real city environments. J. Supercomput., 1–21 (2014)
Thangavelul, A., Bhuvaneswari, K., Kumar, K., et al.: Location Identification and Vehicle Tracking using VANET (VETRAC) (2007)
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)
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)
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)
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)
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)
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)
Artikis, A., Weidlich, M.: Heterogeneous stream processing and crowdsourcing for urban traffic management. In: Edbt (2014)
Suriyapaibonwattana, K., Pomavalai, C.: An effective safety alert broadcast algorithm for VANET. In: International Symposium on Communications & Information Technologies, pp. 247–250. IEEE (2008)
Corporation, H.P.: Mobility crowdsourcing: toward zero-effort carpooling on individual smartphone. Int. J. Distrib. Sens. Netw. 12(3), 188–192 (2013)
Cands, E.J., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717–772 (2009)
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)
Wang, H., Zhu, Y., Zhang, Q.: Compressive sensing based monitoring with vehicular networks. In: Infocom, IEEE, pp. 2823–2831. IEEE (2013)
Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Eprint Arxiv (2010)
Welch, G., Bishop, G.: An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill (1995)
VANET Simulator. http://svs.informatik.uni-hamburg.de/vanet/
OpenStreetMap. http://www.openstreetmap.org
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lin, C., Liu, K., Xu, B., Deng, J., Yu, C.W., Wu, G. (2015). VCLT: An Accurate Trajectory Tracking Attack Based on Crowdsourcing in VANETs. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9530. Springer, Cham. https://doi.org/10.1007/978-3-319-27137-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-27137-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27136-1
Online ISBN: 978-3-319-27137-8
eBook Packages: Computer ScienceComputer Science (R0)