An online target tracking protocol for vehicular Ad Hoc networks

  • Abdessamed DerderEmail author
  • Samira Moussaoui
  • Zouina Doukha
  • Abdelwahab Boualouache


Target tracking in vehicular ad hoc networks (VANETs) contributes to the design of many types of applications, namely: traffic management, security, car recovery and apprehension of an illegal runaway target. Inter-vehicular communication allows vehicles to participate and collaborate in the tracking process. For such applications, a large volume of data can be required to be transferred between the participating vehicles and a control center, which can easily congest the wireless network in a VANET and decrease the tracking efficiency if not managed properly. Therefore, one important challenge in this context is optimizing bandwidth usage to avoid collisions, delays and accelerate the overall tracking process. Thus, we propose a collaborative tracking protocol for VANETs based on a new strategy that we named virtual RSUs, which aims essentially to ensure the network communication coverage during the tracking process on the one hand, and on the other hand, to optimize bandwidth usage during the overall tracking process. In addition, in order to deal with uncertainties and enhance the tracking precision and further decrease the network load, we propose a theoretical pertinence level assignment strategy based on the Transferable Belief Model (TBM), that takes the target detection notifications as inputs. We believe this protocol holds potentials to serve as a basic algorithm to implement vehicle tracking applications for VANETs. Simulative study demonstrates clearly that the proposed protocol provides better performance in terms of network load for target tracking in a VANET as compared to a previous approach.


Vehicular Ad Hoc networks (VANETs) Intelligent transportation system (ITS) Wireless Ad Hod network Mobile Ad Hoc network (MANET) Tracking Virtual RSUs Algorithm design Network protocol 



  1. 1.
    Abdessamed D, Samira M (2014) Target tracking in vanets using v2i and v2v communication. In: 2014 International conference on advanced networking distributed systems and applications (INDS). IEEE, pp 19–24Google Scholar
  2. 2.
    Al-Kuwari S, Wolthusen SD (2010) Probabilistic vehicular trace reconstruction based on rf-visual data fusion. In: IFIP international conference on communications and multimedia security. Springer, pp 16–27Google Scholar
  3. 3.
    Al-Kuwari S, Wolthusen SD (2012) Algorithmic approach for clandestine localisation and tracking in short-range environments. International Journal of Communication Networks and Distributed Systems 9(3-4):311–327CrossRefGoogle Scholar
  4. 4.
    Aravind K G, Chakravarty T, Girish Chandra M, Balamuralidhar P (2009) On the architecture of vehicle tracking system using wireless sensor devices. In: 2009 international conference on ultra modern telecommunications & workshops ICUMT’09. IEEE, pp 1–5Google Scholar
  5. 5.
    Bellavista P, Boukerche A, Campanella T, Foschini L (2017) The trap coverage area protocol for scalable vehicular target tracking. IEEE Access 5:4470–4491CrossRefGoogle Scholar
  6. 6.
    Boukerche A, Oliveira HABF, Nakamura EF, Loureiro AAF (2008) Vehicular ad hoc networks: A new challenge for localization-based systems. Comput Commun 31(12):2838–2849CrossRefGoogle Scholar
  7. 7.
    Chen L-W, Syue K-Z, Tseng Y-C (2010) A vehicular surveillance and sensing system for car security and tracking applications. In: Proceedings of the 9th ACM/IEEE international conference on information processing in sensor networks, pp 426–427. ACMGoogle Scholar
  8. 8.
    Denœux T (2008) Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artif Intell 172(2-3):234–264MathSciNetCrossRefGoogle Scholar
  9. 9.
    Huang-Fu C-C, Chen C-L, Lin Y-B (2010) Location tracking for wave unicast service. In: Vehicular technology conference (VTC 2010-Spring) IEEE 71st. IEEE, p 2010Google Scholar
  10. 10.
    Jiang D, Delgrossi L (2008) IEEE 802.11 p: Towards an international standard for wireless access in vehicular environmentsGoogle Scholar
  11. 11.
    Karp B, Gpsr H-TK (2000) Greedy perimeter stateless routing for wireless networks. In: Proceedings of the 6th annual international conference on mobile computing and networking, pages 243–254. ACMGoogle Scholar
  12. 12.
    Khakpour S, Pazzi RW, El-Khatib K (2017) Using clustering for target tracking in vehicular ad hoc networks. Veh Commun 9:83–96Google Scholar
  13. 13.
    Khakpour S, Pazzi RW, El-Khatib K (2013) A distributed clustering algorithm for target tracking in vehicular ad-hoc networks. In: Proceedings of the 3rd ACM international symposium on design and analysis of intelligent vehicular networks and applications. ACM, pp 145–152Google Scholar
  14. 14.
    Krajzewicz D, Hertkorn G, Rössel C, Wagner P (2002) Sumo (simulation of urban mobility)-an open-source traffic simulation. In: Proceedings of the 4th middle East symposium on simulation and modelling (MESM20002), pp 183–187Google Scholar
  15. 15.
    Manolakis DE (1996) Efficient solution and performance analysis of 3-d position estimation by trilateration. IEEE Trans Aerosp Electron Syst 32(4):1239–1248CrossRefGoogle Scholar
  16. 16.
    Mitra S, Mondal A (2012) Identification, authentication and tracking algorithm for vehicles using vin in distributed vanet. In: Proceedings of the international conference on advances in computing, communications and informatics. ACM, pp 279–286Google Scholar
  17. 17.
    Pongor G (1993) Omnet: Objective modular network testbed. In: Proceedings of the international workshop on modeling, analysis, and simulation on computer and telecommunication systems, pp 323–326. Society for Computer Simulation InternationalGoogle Scholar
  18. 18.
    Popa M, Suta B (2011) A solution for tracking a fleet of vehicles. In: Telecommunications Forum (TELFOR), 2011 19th, pages 1558–1561. IEEEGoogle Scholar
  19. 19.
    Ramos H, Boukerche A, Pazzi R, Frery A, Loureiro A (2012) Cooperative target tracking in vehicular sensor networks. IEEE Wireless Communications, 19(5):66–73Google Scholar
  20. 20.
    Reza TA, Barbeau M, Alsubaihi B (2013) Tracking an on the run vehicle in a metropolitan vanet. In: 2013 intelligent vehicles symposium (IV) IEEE, pp 220–227. IEEEGoogle Scholar
  21. 21.
    Reza TA, Barbeau M, Lamothe G, Alsubaihi B (2013) Non-cooperating vehicle tracking in vanets using the conditional logit model. In: 2013 16th international IEEE conference on intelligent transportation systems-(ITSC). IEEE, pp 626–633Google Scholar
  22. 22.
    Senouci MR, Mellouk A, Oukhellou L, Aissani A (2012) An evidence-based sensor coverage model. IEEE Commun Lett 16(9):1462–1465CrossRefGoogle Scholar
  23. 23.
    Singh J (2011) Technique for privacy preserving real-time vehicle tracking using 802.11 p technology. In: Proceedings of the 9th international conference on advances in mobile computing and multimedia. ACM, pp 206–209Google Scholar
  24. 24.
    Smets P, Kennes R (1994) The transferable belief model. Artif Intell 66(2):191–234MathSciNetCrossRefGoogle Scholar
  25. 25.
    Song H, Zhu S, Cao G (2008) Svats: A sensor-network-based vehicle anti-theft system. In: INFOCOM 2008. The 27th Conference on Computer Communications. IEEE, pp 2128–2136. IEEEGoogle Scholar
  26. 26.
    Subbarao M (1993) Computational methods and electronic camera apparatus for determining distance of objects, rapid autofocusing, and obtaining improved focus images, March 9 Computational US Patent 5, 193, 124Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science, RIIMA LaboratoryUSTHB UniversityAlgiersAlgeria

Personalised recommendations