Abstract
Wireless Sensor Networks (WSNs) can experience problems (anomalies) during deployment, due to dynamic environmental factors or node hardware and software failures. These anomalies demand reliable detection strategies for supporting long term and/or large scale WSN deployments. Several strategies have been proposed for detecting specific subsets of WSN anomalies, yet there is still a need for more comprehensive anomaly detection strategies that jointly address network, node, and data level anomalies. This chapter examines WSN anomalies from an intelligent-based system perspective, covering anomalies that arise at the network, node and data levels. It generalizes a simple process for diagnosing anomalies in WSNs for detection, localization, and root cause determination. A survey of existing anomaly detection strategies also reveals their major design choices, including architecture and user support, and yields guidelines for tailoring new anomaly detection strategies to specific WSN application requirements.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Computer Networks 38(4), 393–422 (2002)
Subramanian, M.: Network Management: An Introduction to Principles and Practice. Addison-Wesley Longman Publishing Co., Inc., Boston (1999)
Rajasegarar, S., Leckie, C., Palaniswami, M.: Anomaly detection in wireless sensor networks. Wireless Communications, 34–40 (August 2008)
Prokopenko, M., Wang, P., Foreman, M., Valencia, P., Price, D.C., Poulton, G.T.: On connectivity of reconfigurable impact networks in ageless aerospace vehicles. Journal of Robotics and Autonomous Systems 53(1), 36–58 (2005)
Rost, S., Balakrishnan, H.: Memento: A Health Monitoring System for Wireless Sensor Networks. In: SECON 2006, Reston, VA, pp. 575–584 (September 2006)
Ramanathan, N., Chang, K., Kapur, R., Girod, L., Kohler, E., Estrin, D.: Sympathy for the sensor network debugger. In: SenSys 2005, pp. 255–267. ACM Press, New York (2005)
Wälchli, M., Braun, T.: Efficient signal processing and anomaly detection in wireless sensor networks. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 81–86. Springer, Heidelberg (2009)
Krishnamachari, B., Iyengar, S.: Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE T. on Computers 53, 241–250 (2004)
Ramanathan, N., Balzano, L., et al.: Rapid deployment with confidence: Calibration and fault detection in environmental sensor networks. UCLA CENS, Tech. Rep. (January 2006)
Chen, J., Kher, S., Somani, A.: Distributed fault detection of wireless sensor networks. In: DIWANS 2006, pp. 65–72. ACM, New York (2006)
Gnawali, O., Fonseca, R., Jamieson, K., Moss, D., Levis, P.: The collection tree protocol. In: Sensys, pp. 1–14. ACM, New York (2009)
Thubert, P.: Draft ietf roll standard (February 2010), http://tools.ietf.org/wg/roll/draft-ietf-roll-of0/
Levis, P., Patel, N., Culler, D., Shenker, S.: Trickle: a self-regulating algorithm for code propagation and maintenance in wireless sensor networks. In: NSDI 2004: Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation, pp. 2–2. USENIX Association, Berkeley (2004)
Dunbabin, M., Udy, J., Grinham, A., Bruenig, M.: Continuous monitoring of reservoir water quality: The wivenhoe project. Journal of the Australian Water Association 36, 74–77 (2009)
Jurdak, R., Ruzzelli, A., Baribirato, A., Boivineau, S.: Octopus: monitoring, visualization, and control of sensor networks. Wireless Communication and Mobile Computing, 1–21 (2009)
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)
Rajasegarar, S., Leckie, C., Palaniswami, M., Bezdek, J.C.: Distributed anomaly detection in wireless sensor networks. In: ICCS 2006, pp. 1–5 (October 2006)
Chang, M., Terzis, A., Bonnet, P.: Mote-based online anomaly detection using echo state networks. Distributed Computing in Sensor Systems, 72–86 (2009)
Obst, O.: Construction and training of a recurrent neural network. Australian Provisional Patent Application 2009902733 (June 2009)
Obst, O.: Distributed backpropagation-decorrelation learning. In: NIPS Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets (2009)
Corke, P., Wark, T., Jurdak, R., Hu, W., Valencia, P., Moore, D.: Environmental Wireless Sensor Networks. Proceedings of the IEEE 98(11), 1903–1917 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Jurdak, R., Wang, X.R., Obst, O., Valencia, P. (2011). Wireless Sensor Network Anomalies: Diagnosis and Detection Strategies. In: Tolk, A., Jain, L.C. (eds) Intelligence-Based Systems Engineering. Intelligent Systems Reference Library, vol 10. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17931-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-17931-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17930-3
Online ISBN: 978-3-642-17931-0
eBook Packages: EngineeringEngineering (R0)