Building Intrusion Detection with a Wireless Sensor Network

  • Markus Wälchli
  • Torsten Braun
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 28)


This paper addresses the detection and reporting of abnormal building access with a wireless sensor network. A common office room, offering space for two working persons, has been monitored with ten sensor nodes and a base station. The task of the system is to report suspicious office occupation such as office searching by thieves. On the other hand, normal office occupation should not throw alarms. In order to save energy for communication, the system provides all nodes with some adaptive short-term memory. Thus, a set of sensor activation patterns can be temporarily learned. The local memory is implemented as an Adaptive Resonance Theory (ART) neural network. Unknown event patterns detected on sensor node level are reported to the base station, where the system-wide anomaly detection is performed. The anomaly detector is lightweight and completely self-learning. The system can be run autonomously or it could be used as a triggering system to turn on an additional high-resolution system on demand. Our building monitoring system has proven to work reliably in different evaluated scenarios. Communication costs of up to 90% could be saved compared to a threshold-based approach without local memory.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lee, D.U., Kim, H., Tu, S., Rahimi, M., Estrin, D., Villasenor, J.D.: Energy-optimized image communication on resource-constrained sensor platforms. In: Proc. of the 6th international conference on Information processing in sensor networks (IPSN 2007), Cambridge, Massachusetts, USA, pp. 216–225 (2007)Google Scholar
  2. 2.
    Chen, M., Leung, V.C.M., Mao, S., Yuan, Y.: Directional geographical routing for real-time video communications in wireless sensor networks. Computer Communications 30(3368-3383), 17 (2007)Google Scholar
  3. 3.
    Xie, D., Yan, T., Ganesan, D., Hanson, A.: Design and implementation of a dual-camera wireless sensor network for object retrieval. In: Proc. of the 7th international conference on Information processing in sensor networks (IPSN 2008), Washington, DC, USA, pp. 469–480 (2008)Google Scholar
  4. 4.
    Luh, W., Kundur, D., Zourntos, T.: A novel distributed privacy paradigm for visual sensor networks based on sharing dynamical systems. EURASIP Journal of Applied Signal Processing 2007(1), 1–17 (2007)CrossRefMATHGoogle Scholar
  5. 5.
    Basharat, A., Catbas, N., Shah, M.: A framework for intelligent sensor network with video camera for structural health monitoring of bridges. In: Proc. of the Third IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW 2005), Washington, DC, USA, pp. 385–389 (2005)Google Scholar
  6. 6.
    Li, D., Wong, K.D., Hu, Y.H., Sayeed, A.M.: Detection, classification and tracking of targets. IEEE Signal Processing Magazine 19(2), 17–29 (2002)CrossRefGoogle Scholar
  7. 7.
    Wang, T.Y., Han, Y.S., Varshney, P.K., Chen, P.N.: Distributed fault-tolerant classification in wireless sensor networks. IEEE Journal on Selected Areas in Communications 23(4), 724–734 (2005)CrossRefGoogle Scholar
  8. 8.
    Wittenburg, G., Terfloth, K., Villafuerte, F.L., Naumowicz, T., Ritter, H., Schiller, J.: Fence monitoring - experimental evaluation of a use case for wireless sensor networks. In: Langendoen, K.G., Voigt, T. (eds.) EWSN 2007. LNCS, vol. 4373, pp. 163–178. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Wälchli, M., Braun, T.: Event classification and filtering of false alarms in wireless sensor networks. In: Proc. of 3rd International Workshop on Intelligent Systems Techniques for Ad hoc and Wireless Sensor Networks (IST-AWSN 2008), Sydney, Australia (December 2008)Google Scholar
  10. 10.
    Kulakov, A., Davcev, D.: Intelligent wireless sensor networks using fuzzyart neural-networks. In: Proc. of the 12th IEEE Symposium on Computers and Communications (ISCC 2007), Aveiro, Portugal, July 2007, pp. 569–574 (2007)Google Scholar
  11. 11.
    Römer, K.: Discovery of frequent distributed event patterns in sensor networks. In: Verdone, R. (ed.) EWSN 2008. LNCS, vol. 4913, pp. 106–124. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Benbasat, A.Y., Paradiso, J.A.: A framework for the automated generation of power-efficient classifiers for embedded sensor nodes. In: Proc. of the 5th international conference on Embedded networked sensor systems (SenSys 2007), Sydney, Australia, pp. 219–232 (2007)Google Scholar
  13. 13.
    Gu, L., Jia, D., Vicaire, P., Yan, T., Luo, L., Tirumala, A., Cao, Q., He, T., Stankovic, J.A., Abdelzaher, T., Krogh, B.H.: Lightweight detection and classification for wireless sensor networks in realistic environments. In: Proc. of the 3rd international conference on Embedded networked sensor systems (SenSys 2005), San Diego, California, USA, November 2005, pp. 205–217 (2005)Google Scholar
  14. 14.
    Cai, J., Ee, D., Pham, B., Roe, P., Zhang, J.: Sensor network for the monitoring of ecosystem: Bird species recognition. In: Proc. of the 3rd International Conference on Intelligent Sensors, Sensor Networks and Information (ISSNIP 2007), Melbourne, Australia, pp. 293–298 (2007)Google Scholar
  15. 15.
    Powell, G., Marshall, D., Smets, P., Ristic, B., Maskell, S.: Joint tracking and classification of airbourne objects using particle filters and the continuous transferable belief model. In: Proc. of the 2006 9th International Conference on Information Fusion (Fusion 2006), Florence, Italy, July 2006, pp. 1–8 (2006)Google Scholar
  16. 16.
    Wang, X.R., Lizier, J.T., Obst, O., Prokopenko, M., Wang, P.: Spatiotemporal anomaly detection in gas monitoring sensor networks. In: Verdone, R. (ed.) EWSN 2008. LNCS, vol. 4913, pp. 90–105. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Patcha, A., Park, J.M.: An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks 51(12), 3448–3470 (2007)CrossRefGoogle Scholar
  18. 18.
    Roman, R., Zhou, J., Lopez, J.: Applying intrusion detection systems to wireless sensor networks. In: Proc. of the 3rd IEEE Consumer Communications and Networking Conference (CCNC 2006), Las Vegas, Nevada, USA, January 2006, vol. 1, pp. 640–644 (2006)Google Scholar
  19. 19.
    Krontiris, I., Dimitriou, T., Giannetsos, T., Mpasoukos, M.: Intrusion Detection of Sinkhole Attacks in Wireless Sensor Networks. In: Algorithmic Aspects of Wireless Sensor Networks, pp. 150–161. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  20. 20.
    Li, Y.Y., Parker, L.: Intruder detection using a wireless sensor network with an intelligent mobile robot response. In: Proc. of the IEEE SoutheastCon 2008, Huntsville, Alabama, USA, pp. 37–42 (2008)Google Scholar
  21. 21.
    Dasgupta, D., Forrest, S.: Novelty detection in time series data using ideas from immunology. In: Proc. of The Fifth International Conference on Intelligent Systems (IS 1996), Reno, Nevada, USA (1996)Google Scholar
  22. 22.
    Mazhar, N., Farooq, M.: A sense of danger: dendritic cells inspired artificial immune system for manet security. In: Proc. of the 10th annual conference on Genetic and evolutionary computation (GECCO 2008), Atlanta, GA, USA, pp. 63–70 (2008)Google Scholar
  23. 23.
    Burwick, T., Joublin, F.: Optimal algorithmic complexity of fuzzy art. Neural Processing Letters 7(1), 37–41 (1998)CrossRefGoogle Scholar
  24. 24.
    Scatterweb: The self-organizing wireless communication platform (October 2007)Google Scholar
  25. 25.
    Carpenter, G., Grossberg, S.: Adaptive Resonance Theory. Bradford Books. MIT Press, Cambridge (2002)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2010

Authors and Affiliations

  • Markus Wälchli
    • 1
  • Torsten Braun
    • 1
  1. 1.Institute of Computer Science and Applied MathematicsUniversity of BernSwitzerland

Personalised recommendations