Advertisement

Forensic Tracking and Mobility Prediction in Vehicular Networks

  • Saif Al-Kuwari
  • Stephen Wolthusen
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 383)

Abstract

Most contemporary tracking applications consider an online approach where the target is tracked in real time. In criminal investigations, however, it is often the case that only offline tracking is possible, i.e., tracking takes place after the fact. In offline tracking, given an incomplete trace of a target, the task is to reconstruct the missing parts and obtain the full trace. The proliferation of modern transportation systems means that a targeted entity is likely to use multiple modes of transportation. This paper introduces a class of mobility models tailored for forensic analysis. The mobility models are used to construct a multi-modal forensic tracking system that can reconstruct a complete trace of a target. Theoretical analysis of the reconstruction algorithm demonstrates that it is both complete and optimal.

Keywords

Forensic tracking mobility models trace reconstruction 

References

  1. 1.
    S. Al-Kuwari and S. Wolthusen, Probabilistic vehicular trace reconstruction based on RF-visual data fusion, Proceedings of the Eleventh IFIP TC 6/TC 11 International Conference on Communications and Multimedia Security, pp. 16–27, 2010.Google Scholar
  2. 2.
    S. Al-Kuwari and S. Wolthusen, Fuzzy trace validation: Toward an offline forensic tracking framework, Proceedings of the Sixth IEEE International Workshop on Systematic Approaches to Digital Forensic Engineering, 2011.Google Scholar
  3. 3.
    A. Dempster, N. Laird and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B (Methodological), vol. 39(1), pp. 1–38, 1977.MathSciNetMATHGoogle Scholar
  4. 4.
    W. Gilks, S. Richardson and D. Spiegelhalter (Eds.), Markov Chain Monte Carlo in Practice, Chapman and Hall/CRC Press, Boca Raton, Florida, 1996.MATHGoogle Scholar
  5. 5.
    J. Harri, F. Filali and C. Bonnet, Mobility models for vehicular ad hoc networks: A survey and taxonomy, IEEE Communications Surveys and Tutorials, vol. 11(4), pp. 19–41, 2009.CrossRefGoogle Scholar
  6. 6.
    D. Helbing and P. Molnar, Social force model for pedestrian dynamics, Physical Review E, vol. 51(5), pp. 4282–4286, 1995.CrossRefGoogle Scholar
  7. 7.
    M. Tanner and W. Wong, The calculation of posterior distributions by data augmentation, Journal of the American Statistical Association, vol. 82(398), pp. 528–540, 1987. MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Saif Al-Kuwari
    • 1
  • Stephen Wolthusen
    • 2
    • 3
  1. 1.Ministry of Foreign AffairsDohaQatar
  2. 2.Norwegian Information Security LaboratoryGjovik University CollegeGjovikNorway
  3. 3.Royal HollowayUniversity of LondonLondonUnited Kingdom

Personalised recommendations