Abstract
We develop a practical, distributed algorithm to detect events, identify measurement errors, and infer missing readings in ecological applications of wireless sensor networks. To address issues of non-stationarity in environmental data streams, each sensor-processor learns statistical distributions of differences between its readings and those of its neighbors, as well as between its current and previous measurements. Scalar physical quantities such as air temperature, soil moisture, and light flux naturally display a large degree of spatiotemporal coherence, which gives a spectrum of fluctuations between adjacent or consecutive measurements with small variances. This feature permits stable estimation over a small state space. The resulting probability distributions of differences, estimated online in real time, are then used in statistical significance tests to identify rare events. Utilizing the spatio-temporal distributed nature of the measurements across the network, these events are classified as single mode failures - usually corresponding to measurement errors at a single sensor - or common mode events. The event structure also allows the network to automatically attribute potential measurement errors to specific sensors and to correct them in real time via a combination of current measurements at neighboring nodes and the statistics of differences between them. Compared to methods that use Bayesian classification of raw data streams at each sensor, this algorithm is more storage-efficient, learns faster, and is more robust in the face of non-stationary phenomena. Field results from a wireless sensor network (Sensor Web) deployed at Sevilleta National Wildlife Refuge are presented.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
Szewczyk, R., Osterweil, E., Polastre, J., Hamilton, M., Mainwaring, A., Estrin, D.: Habitat monitoring with sensor networks. Communications of the ACM 47(6), 34–40 (2004)
Delin, K.A.: Sensor Webs in the wild. In: Bulusu, N., Jha, S. (eds.) Wireless Sensor Networks: A Systems Perspective. Artech House (2005)
Marzullo, K.: Tolerating failures of continuous-valued sensors. ACM Trans. Comput. Syst. 8(4), 284–304 (1990)
Elnahrawy, E., Nath, B.: Cleaning and querying noisy sensors. In: Proceedings of the Second ACM International Workshop on Wireless Sensor Networks and Applications, ACM Press, New York (2003)
Bychkovskiy, V., Megerian, S., Estrin, D., Potkonjak, M.: A collaborative approach to in-place sensor calibration. In: Zhao, F., Guibas, L.J. (eds.) IPSN 2003. LNCS, vol. 2634, pp. 301–316. Springer, Heidelberg (2003)
Sharma, A., Leana Golubchik, R.G.: On the prevalence of sensor faults in real world deployments. In: Proceedings of the IEEE Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON) (June 2007)
Jeffery, S.R., Alonso, G., Franklin, M.J., Hong, W., Widom, J.: Declarative support for sensor data cleaning. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 83–100. Springer, Heidelberg (2006)
Estrin, D., Culler, D., Pister, K., Sukhatme, G.: Connecting the physical world with pervasive networks. IEEE Pervasive Computing 1(1), 59–69 (2002)
Tulone, D., Madden, S.: An energy-efficient querying framework in sensor networks for detecting node similarities. In: MSWiM’06 (2006)
Delin, K.A.: The Sensor Web: A macro-instrument for coordinated sensing. Sensors 2, 270–285 (2002)
Delin, K.A., Jackson, S.P., Johnson, D.W., Burleigh, S.C., Woodrow, R.R., McAuley, J.M., Dohm, J.M., Ip, F., Ferre, T.P.A., Rucker, D.F., Baker, V.R.: Environmental studies with the Sensor Web: Principles and practice. Sensors 5, 103–117 (2005)
Meguerdichian, S., Slijepcevic, S., Karayan, V., Potkonjak, M.: Localized algorithms in wireless ad-hoc networks: Location discovery and sensor exposure. In: Proceedings of MobiHOC 2001, Long Beach, CA, pp. 106–116 (2001)
Collins, S.L., Bettencourt, L.M.A., Hagberg, A., Brown, R.F., Moore, D.I., Delin, K.A.: New opportunities in ecological sensing using wireless sensor networks. Frontiers in Ecology 4(8), 402–407 (2006)
Szewczyk, R., Polastre, J., Mainwaring, A., Culler, D.: Lessons from a sensor network expedition. In: Karl, H., Wolisz, A., Willig, A. (eds.) Wireless Sensor Networks. LNCS, vol. 2920, Springer, Heidelberg (2004)
Ramanathan, N., Balzano, L., Burt, M., Estrin, D., Harmon, T., Harvey, C., Jay, J., Kohler, E., Rothenberg, S., Srivastava, M.: Rapid deployment with confidence:calibration and fault detection in environmental sensor networks. Technical Report 62, CENS, UCLA (2006)
Werner-Allen, G., Lorincz, K., Johnson, J., Lees, J., Welsh, M.: Fidelity and yield in a volcano monitoring sensor network. In: Proceedings of the 7th USENIX Symposium on Operating Symposium (OSDI 2006) (2006)
Deshpande, A., Guestrin, C., Madden, S.R., Hellerstein, J.M., Hong, W.: Model-driven data acquisition in sensor networks. In: 30th International Conference on Very Large Data Bases, pp. 588–599 (2004)
Liu, K., Sayeed, A.: Asymptotically optimal decentralized type-based detection in wireless sensor networks. In: Acoustics, Speech, and Signal Processing, IEEE International Conference (ICASSP ’04), vol. 3, pp. 873–876 (2004)
Gupta, H., Navda, V., Das, S.R., Chowdhary, V.: Efficient gathering of correlated data in sensor networks. In: MobiHoc ’05: Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing, pp. 402–413. ACM Press, New York (2005)
Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: A systematic survey. IEEE Trans. on Image Proc. 14(3) (2005)
Markou, M., Singh, S.: Novelty detection: A review - part 1: Statistical approaches. Signal Process. 83(12), 2481–2497 (2003)
Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis, 2nd edn. CRC Press, Boca Raton (2003)
Elnahrawy, E., Nath, B.: Context-aware sensors. In: Karl, H., Wolisz, A., Willig, A. (eds.) Wireless Sensor Networks. LNCS, vol. 2920, pp. 77–93. Springer, Heidelberg (2004)
DeGroot, M.H.: Optimal Statistical Decisions. Wiley, Chichester (2004)
Maybeck, P.S.: Stochastic Models, Estimation, and Control. In: Mathematics in Science and Engineering, vol. 141, Academic Press, San Diego (1979)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, Chichester (2000)
Rice, W.R.: A consensus combined p-value test and the family-wide significance of component tests. Biometrics 46(2), 303–308 (1990)
Folks, L.J.: Combination of independent tests. In: Krishnaiah, P.R., Sen, P.K. (eds.) Handbook of Statistics 4. Nonparametric Methods, North Holland, New York (1984)
Lakhina, A., Crovella, M., Diot, C.: Diagnosing network-wide traffic anomalies. SIGCOMM Comput. Commun. Rev. 34(4), 219–230 (2004)
Hedges, L.V., Olkin, I.: Statistical Method for Meta-Analysis. Academic Press, San Diego (1985)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Bettencourt, L.M.A., Hagberg, A.A., Larkey, L.B. (2007). Separating the Wheat from the Chaff: Practical Anomaly Detection Schemes in Ecological Applications of Distributed Sensor Networks. In: Aspnes, J., Scheideler, C., Arora, A., Madden, S. (eds) Distributed Computing in Sensor Systems. DCOSS 2007. Lecture Notes in Computer Science, vol 4549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73090-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-73090-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73089-7
Online ISBN: 978-3-540-73090-3
eBook Packages: Computer ScienceComputer Science (R0)