Wireless Networks

, Volume 18, Issue 3, pp 335–349 | Cite as

Determining sink location through Zeroing-In attackers in wireless sensor networks

Article

Abstract

The viability and success of wireless sensor networks critically hinge on the ability of a small number of sinks to glean sensor data throughout the networks. Thus, the locations of sinks are critically important. In this paper, we examine the sink location privacy problem from both the attack and defense perspectives. First, we examine resource-constrained adversaries who can only eavesdrop the network at their vicinities. To determine the sink location, they can launch a Zeroing-In attack by leveraging the fact that several network metrics are 2-dimensional functions in the plane of the network, and their values minimize at the sink. Thus, determining the sink location is equivalent to finding the minima of those functions. We demonstrate that by obtaining the hop counts or the arrival time of a broadcast packet at a few spots in the network, the adversaries are able to determine the sink location with the accuracy of one radio range, which is sufficient to disable the sink by launching jamming attacks, for example. To cope with the Zeroing-In attacks, we present a directed-walk-based routing scheme and show that the defense strategy is effective in deceiving adversaries at little energy costs.

References

  1. 1.
    Mainwaring, A., Mainwaring, A., Polastre, J., Polastre, J. Szewczyk, R., Szewczyk, R., Culler, D., & Culler, D. (2002). Wireless sensor networks for habitat monitoring. pp. 88–97.Google Scholar
  2. 2.
    Xu, N., Rangwala, S., Chintalapudi, K.K., Ganesan, D., Broad, A., Govindan, R., & Estrin, D. (2004). A wireless sensor network for structural monitoring. In SenSys ’04: In Proceedings of the 2nd international conference on Embedded networked sensor systems (pp. 13–24). New York, NY, USA: ACM.Google Scholar
  3. 3.
    He, T., Vicaire, P., Yan, T., Luo, L., Gu, L., Zhou, G., Stoleru, R., Cao, Q., Stankovic, J. A., & Abdelzaher, T. (2006). Achieving real-time target tracking usingwireless sensor networks. In RTAS ’06: Proceedings of the 12th IEEE real-time and embedded technology and applications Symposium (pp. 37–48). Washington, DC, USA: IEEE Computer Society.Google Scholar
  4. 4.
    Xu, W., Trappe, W., Zhang, Y., & Wood, T. (2005). The feasibility of launching and detecting jamming attacks in wireless networks. In MobiHoc ’05: Proceedings of the 6th ACM international Symposium on mobile ad hoc networking and computing (pp. 46–57).Google Scholar
  5. 5.
    Kamat, P., Zhang, Y., Trappe, W., & Ozturk, C. (2005). Enhancing source-location privacy in sensor network routing. In ICDCS ’05: Proceedings of the 25th IEEE international conference on distributed computing systems (pp. 599–608). Washington, DC, USA: IEEE Computer Society.Google Scholar
  6. 6.
    Deng, J., Han, R., & Mishra, S. (2005). Countermeasures against traffic analysis attacks in wireless sensor networks. In SECURECOMM ’05: Proceedings of the first international conference on security and privacy for emerging areas in communications networks (pp. 113–126). Washington, DC, USA: IEEE Computer Society.Google Scholar
  7. 7.
    Mehta, K., Liu, D., & Wright, M. (2007). Icnp’07: Location privacy in sensor networks against a global eavesdropper. In Proceedings of the IEEE international conference on network protocols (pp. 314–323).Google Scholar
  8. 8.
    Yang, Y. Shao, M., Zhu, S., Urgaonkar, B., & Cao, G. (2008). Towards event source unobservability with minimum network traffic in sensor networks. In WiSec ’08: Proceedings of the first ACM conference on wireless network security (pp. 77–88). New York, NY, USA: ACM.Google Scholar
  9. 9.
    Reed, M., Syverson, & P., Goldschlag, D. (1998). Anonymous connections and onion routing. IEEE Journal on Selected Areas in Communications, 16, 482–494.Google Scholar
  10. 10.
    Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L., & Rubenstein, D. (2002). Energy-efficient computing for wildlife tracking: Design and tradeoffs and early experiences with Zebranet. In: Proceedings of the tenth international conference on architectural support for programming languages and operating systems (pp. 96–107).Google Scholar
  11. 11.
    Madden, S., Franklin, M., Hellerstein, J., & Hong W. (2002). TAG: a tiny aggregation service for Ad-Hoc sensor networks. In Proceedings of the usenix Symposium on operating systems design and implementation.Google Scholar
  12. 12.
    Zhao, J., & Govindan, R. (2003). Understanding packet delivery performance in dense wireless sensor networks. In SenSys ’03: Proceedings of the 1st international conference on embedded networked sensor systems (pp. 1–13).Google Scholar
  13. 13.
    Trappe, W., & Washington, L. (2002). Introduction to cryptography with coding theory. Upper Saddle River: Prentice Hall.Google Scholar
  14. 14.
    Woo, A., Tong, T., & Culler, D. (2003). Taming the underlying challenges of reliable multihop routing in sensor networks. In SenSys ’03: Proceedings of the 1st international conference on embedded networked sensor systems (pp. 14–27).Google Scholar
  15. 15.
  16. 16.
    Boulis, A. (2007). Castalia: revealing pitfalls in designing distributed algorithms in wsn. In SenSys ’07: Proceedings of the 5th international conference on embedded networked sensor systems (pp. 407–408). New York, NY, USA: ACM.Google Scholar
  17. 17.
    OMNeT++ homepage. http://www.omnetpp.org/.
  18. 18.
    Zuniga, M., & Krishnamachari, B. (2004). Analyzing the transitional region in low power wireless links. In SECON’04: Proceedings of (pp. 517–526).Google Scholar
  19. 19.
    Xing, G., Wang, X., Zhang, Y., Pless, C.L.R., Gill, C. (2005). Integrated coverage and connectivity configuration for energy conservation in sensor networks. ACM Transaction of Sen-sor Networks, 1(1), 36–72.CrossRefGoogle Scholar
  20. 20.
    Nicelescu, D., & Nath, B. (2003). DV based positioning in ad hoc networks. Telecommunication Systems, 22(1-4), 267–280.CrossRefGoogle Scholar
  21. 21.
    Kamat, P., Xu, W., Trappe, W., & Zhang, Y. (2007). Temporal privacy in wireless sensor networks. In Proceedings of the 27th international conference on distributed computing systems, ICDCS ’07 (pp. 23– 25). Washington, DC, USA: IEEE Computer Society.Google Scholar
  22. 22.
  23. 23.
    Gruteser, M., & Grunwald, D. (2003). Anonymous usage of location-based services through spatial and temporal cloaking. In MobiSys ’03: Proceedings of the 1st international conference on Mobile systems, applications and services (pp. 31–42). New York, NY, USA: ACM.Google Scholar
  24. 24.
    Hoh, B., Gruteser, & M. (2005). Protecting location privacy through path confusion. In SECURECOMM ’05: Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks (pp. 194–205). Washington, DC, USA: IEEE Computer Society.Google Scholar
  25. 25.
    Ouyang, Y., Le, Z., Liu, D., Ford, & J., Makedon, F. (2008) Source location privacy against laptop-class attacks in sensor networks. In SecureComm ’08: Proceedings of the 4th international conference on Security and privacy in communication netowrks (pp. 1–10) New York, NY, USA: ACM.Google Scholar
  26. 26.
    Shao, M., Yang, Y., Zhu, S., & Cao, G. (2008). Towards statistically strong source anonymity for sensor networks. In INFOCOM’08: 27th IEEE international conference on computer communications (pp. 51–55).Google Scholar
  27. 27.
    Deng, J., Han, R., & Mishra, S. (2004). Intrusion tolerance and anti-traffic analysis strategies for wireless sensor networks. In DSN ’04: Proceedings of the 2004 international conference on dependable systems and networks (pp. 637). Washington, DC, USA: IEEE Computer Society.Google Scholar
  28. 28.
    Jian, Y., Chen, S., Zhang, & Z., Zhang L. (2007). Protecting receiver-location privacy in wireless sensor networks. In INFOCOM’07: 26th IEEE international conference on computer communications) (pp. 1955–1963).Google Scholar
  29. 29.
    Conner, W., Abdelzaher, T., & Nahrstedt, K. (2006). Using data aggregation to prevent traffic analysis in wireless sensor networks. In DCOSS ’06: international conference on distributed computing in sensor networks.Google Scholar
  30. 30.
    Priyantha, N. B., Chakraborty, A., & Balakrishnan, H. (2000). The cricket location-support system. In 6th ACM MOBICOM, Boston, MA.Google Scholar
  31. 31.
    Liu, Z., & Xu, W. (2010). Zeroing-in on network metric minima for sink location determination. In Proceedings of the third ACM conference on wireless network security, WiSec ’10 (pp. 99–104). New York, NY, USA: ACM.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of South CarolinaColumbiaUSA

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