Computer Science - Research and Development

, Volume 24, Issue 3, pp 153–170 | Cite as

In-network detection of anomaly regions in sensor networks with obstacles

  • Conny Franke
  • Marcel Karnstedt
  • Daniel Klan
  • Michael Gertz
  • Kai-Uwe Sattler
  • Elena Chervakova
Open Access
Special Issue Paper


In the past couple of years, sensor networks have evolved into an important infrastructure component for monitoring and tracking events and phenomena in several, often mission critical application domains. An important task in processing streams of data generated by these networks is the detection of anomalies, e.g., outliers or bursts, and in particular the computation of the location and spatial extent of such anomalies in a sensor network. Such information is then used as an important input to decision making processes.

In this paper, we present a novel approach that facilitates the efficient computation of such anomaly regions from individual sensor readings. We propose an algorithm to derive regions with a spatial extent from individual (anomalous) sensor readings, with a particular focus on obstacles present in the sensor network and the influence of such obstacles on anomaly regions. We then improve this approach by describing a distributed in-network processing technique where the region detection is performed at sensor nodes and thus leads to important energy savings. We demonstrate the advantages of this strategy over a traditional, centralized processing strategy by employing a cost model for real sensors and sensor networks.


Sensor networks  Data streams   Outlier detection  Distributed computation 


  1. 1.
    MIT Computer Science and Artificial Intelligence Lab (2004) Intel lab sensor data. Scholar
  2. 2.
    California Irrigation Management Information System (CIMIS) (2008) Scholar
  3. 3.
    Angiulli F, Fassetti F (2007) Detecting distance-based outliers in streams of data. In: Proc. 16th ACM Conference on Conference on Information and Knowledge Management (CIKM’07), New York, pp. 811–820Google Scholar
  4. 4.
    Basu S, Meckesheimer M (2007) Automatic outlier detection for time series: an application to sensor data. Knowl Inf Sys 11(2):137–154Google Scholar
  5. 5.
    Kant Chintalapudi K, Govindan R (2003) Localized edge detection in sensor fields. In: Proc. 1st IEEE Int. Workshop on Sensor Network Protocols and Applications, May 2003, pp. 59–70Google Scholar
  6. 6.
    Culler D, Estrin D, Srivastava M (2004) Overview of sensor networks. IEEE Comput 37(8):41–49Google Scholar
  7. 7.
    Singh Dhillon S, Chakrabarty K (2003) Sensor placement for effective coverage and surveillance in distributed sensor networks. In: Proc. of IEEE Wireless Communications and Networking Conference, pp. 1609–1614Google Scholar
  8. 8.
    Ding M, Chen D, Xing K, Cheng X (2005) Localized fault-tolerant event boundary detection in sensor networks. In: Proc. of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies INFOCOM 2005, March 2005, Vol. 2, pp. 902–913Google Scholar
  9. 9.
    Estivill-Castro V, Lee I (2001) Fast spatial clustering with different metrics and in the presence of obstacles. In: Proc. 9th ACM Int. Symposium on Advances in Geographic Information Systems (ACM-GIS’01), Atlanta, pp. 142–147Google Scholar
  10. 10.
    Franke C, Gertz M (2008) Detection and exploration of outlier regions in sensor data streams. In Proc. Int. Workshop on Spatial and Spatiotemporal Data Mining (SSTDM’08), Dec 2008Google Scholar
  11. 11.
    Gehrke J, Madden S (2004) Query processing in sensor networks. IEEE Pervas Comput 3(1):46–55CrossRefGoogle Scholar
  12. 12.
    Gu L, Stankovic JA (2005) Radio-triggered wake-up for wireless sensor networks. Real-time Sys 29:157–182CrossRefGoogle Scholar
  13. 13.
    Han J, Kamber M, Tung AKH (2001) Spatial Clustering Methods in Data Mining: A Survey. In: Miller H, Han J (eds) Geographic Data Mining and Knowledge Discovery. CRC Press, pp. 201–230Google Scholar
  14. 14.
    Klan D, Karnstedt M, Pölitz C, Sattler KU (2008) Towards burst detection for non-stationary stream data. In: Proc. Workshop on Knowledge Discovery, Data Mining, and Machine Learning (KDML 2008)Google Scholar
  15. 15.
    Kolingerová I, Zalik B (2006) Reconstructing domain boundaries within a given set of points, using delaunay triangulation. Comput Geosci 32(9):1310–1319CrossRefGoogle Scholar
  16. 16.
    Krause A, Guestrin C, Gupta A, Kleinberg J (2006) Near-optimal sensor placements: maximizing information while minimizing communication cost. In: Proc. of the 5th Int. Conference on Information Processing in Sensor Networks (IPSN ’06), New York, pp. 2–10Google Scholar
  17. 17.
    Krishnamachari B, Estrin D, Wicker SB (2002) The impact of data aggregation in wireless sensor networks. In: ICDCSW ’02, Washington, DC, pp. 575–578Google Scholar
  18. 18.
    Lu C-T, Kou Y, Zhao J, Chen L (2007) Detecting and tracking regional outliers in meteorological data. Inf Sci 177(7):1609–1632CrossRefGoogle Scholar
  19. 19.
    Nowak R, Mitra U (2003) Boundary estimation in sensor networks: Theory and methods. Inform Proc Sens Netw 2634:80–95CrossRefGoogle Scholar
  20. 20.
    Shasha D, Zhu Y (2004) High Performance Discovery in Time Series: Techniques and Case Studies. Springer, BerlinGoogle Scholar
  21. 21.
    Subramaniam S, Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D (2006) Online outlier detection in sensor data using non-parametric models. In: Proc. Int. Conf. on Very Large Data Bases (VLDB’06), pp. 187–198Google Scholar
  22. 22.
    Tung AKH, Hou J, Han J (2001) Spatial clustering in the presence of obstacles. In: Proc. 17th Int. Conference on Data Engineering (ICDE’01), Washington, DC, IEEE Computer Society, pp. 359–367Google Scholar
  23. 23.
    Wu W, Cheng X, Ding M, Xing K, Liu F, Deng P (2007) Localized outlying and boundary data detection in sensor networks. IEEE Trans Knowl Data Engin 19(8):1145–1157CrossRefGoogle Scholar
  24. 24.
    Yao Y, Gehrke J (2003) Query processing for sensor networks. In: Proc. of the 1st Biennial Conference on Innovative Data Systems Research (CIDR’03), January 2003Google Scholar
  25. 25.
    Yao Y, Gehrke J (2002) The cougar approach to in-network query processing in sensor networks. ACM SIGMOD Record 31(3):9–18Google Scholar
  26. 26.
    Zhang J, Lou M, Ling TW, Wang H (2004) Hos-miner: a system for detecting outlyting subspaces of high-dimensional data. In: Proc. Int. Conf. on Very Large Data Bases (VLDB’04), VLDB Endowment, pp. 1265–1268Google Scholar
  27. 27.
    Zhang J, Papadias D, Mouratidis K, Zhu M (2004) Spatial queries in the presence of obstacles. In: Proc. 9th Int. Conference on Extending Database Technology (EDBT’04), Heraklion, Crete, Greece, pp. 366–384Google Scholar
  28. 28.
    Zhang X, Shasha D (2006) Better burst detection. In: Proc. 22nd Int. Conference on Data Engineering (ICDE’06), pp. 146–149Google Scholar
  29. 29.
    Zhao F, Liu J, Liu J, Guibas L, Reich J (2003) Collaborative signal and information processing: An information directed approach. Proc IEEE 1199–1209Google Scholar
  30. 30.
    Zhu Y, Shasha D (2003) Efficient elastic burst detection in data streams. In: KDD ’03, New York, pp. 336–345Google Scholar

Copyright information

© The Author(s) 2009

Authors and Affiliations

  • Conny Franke
    • 1
  • Marcel Karnstedt
    • 2
  • Daniel Klan
    • 2
  • Michael Gertz
    • 3
  • Kai-Uwe Sattler
    • 2
  • Elena Chervakova
    • 4
  1. 1.Department of Computer ScienceUniversity of California at DavisDavisUSA
  2. 2.Databases and Information Systems GroupIlmenau University of TechnologyIlmenauGermany
  3. 3.Institute of Computer ScienceUniversity of HeidelbergHeidelbergGermany
  4. 4.Institute of Microelectronics- and Mechatronics SystemsIlmenauGermany

Personalised recommendations