Continuous Probabilistic Count Queries in Wireless Sensor Networks

  • Anna Follmann
  • Mario A. Nascimento
  • Andreas Züfle
  • Matthias Renz
  • Peer Kröger
  • Hans-Peter Kriegel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6849)


Count queries in wireless sensor networks (WSNs) report the number of sensor nodes whose measured values satisfy a given predicate. However, measurements in WSNs are typically imprecise due, for instance, to limited accuracy of the sensor hardware. In this context, we present four algorithms for computing continuous probabilistic count queries on a WSN, i.e., given a query Q we compute a probability distribution over the number of sensors satisfying Q’s predicate. These algorithms aim at maximizing the lifetime of the sensors by minimizing the communication overhead and data processing cost. Our performance evaluation shows that by using a distributed and incremental approach we are able to reduce the number of message transfers within the WSN by up to a factor of 5 when compared to a straightforward centralized algorithm.


Sensor Node Wireless Sensor Network Intermediate Node Child Node Uncertain Data 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hua, M., et al.: Ranking queries on uncertain data: a probabilistic threshold approach. In: Proc. of ACM SIGMOD, pp. 673–686 (2008)Google Scholar
  2. 2.
    Schurgers, C., et al.: Optimizing sensor networks in the energy-latency-density design space. IEEE TMC 1, 70–80 (2002)Google Scholar
  3. 3.
    Madden, S., et al.: Tag: a tiny aggregation service for ad-hoc sensor networks. SIGOPS Operating Systems Review 36, 131–146 (2002)CrossRefGoogle Scholar
  4. 4.
    Dalvi, N., Suciu, D.: Efficient query evaluation on probabilistic databases. The VLDB Journal 16, 523–544 (2007)CrossRefGoogle Scholar
  5. 5.
    Cheng, R., et al.: Efficient indexing methods for probabilistic threshold queries over uncertain data. In: Proc. of VDLB, pp. 876–887 (2004)Google Scholar
  6. 6.
    Kriegel, H.P., et al.: Probabilistic similarity join on uncertain data. In: Li Lee, M., Tan, K.-L., Wuwongse, V. (eds.) DASFAA 2006. LNCS, vol. 3882, pp. 295–309. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Bernecker, T., et al.: Scalable probabilistic similarity ranking in uncertain databases. IEEE TKDE 22(9), 1234–1246 (2010)Google Scholar
  8. 8.
    Sarma, A., et al.: Working models for uncertain data. In: Proc. of IEEE ICDE, pp. 7–7 (2006)Google Scholar
  9. 9.
    Ross, R., Subrahmanian, V.S., Grant, J.: Aggregate operators in probabilistic databases. J. ACM 52, 54–101 (2005)CrossRefzbMATHGoogle Scholar
  10. 10.
    Soliman, M.A., Ilyas, I.F., Chang, K.C.C.: Top-k query processing in uncertain databases. In: Proc. of IEEE ICDE, pp. 896–905 (2007)Google Scholar
  11. 11.
    Yi, K., et al.: Efficient processing of top-k queries in uncertain databases. In: Proc. of IEEE ICDE, pp. 1406–1408 (2008)Google Scholar
  12. 12.
    Cormode, G., Li, F., Yi, K.: Semantics of ranking queries for probabilistic data and expected ranks. In: Proc. of IEEE ICDE, pp. 305–316 (2009)Google Scholar
  13. 13.
    Li, J., Saha, B., Deshpande, A.: A unified approach to ranking in probabilistic databases. Proc. of VLDB 2, 502–513 (2009)CrossRefGoogle Scholar
  14. 14.
    Malhotra, B., Nascimento, M.A., Nikolaidis, I.: Exact top-k queries in wireless sensor networks. IEEE TKDE (2010) (to appear) Google Scholar
  15. 15.
    Ye, W., Heidemann, J., Estrin, D.: An energy-efficient mac protocol for wireless sensor networks. In: Proc. of IEEE INFOCOM, pp. 1567–1576 (2002)Google Scholar
  16. 16.
    Pinedo-Frausto, E., Garcia-Macias, J.: An experimental analysis of zigbee networks. In: 33rd IEEE Conference on Local Computer Networks, LCN 2008, pp. 723–729 (2008)Google Scholar
  17. 17.
    Wang, S., Wang, G., Gao, X., Tan, Z.: Frequent items computation over uncertain wireless sensor network. In: Proc. of ICHIS, pp. 223–228 (2009)Google Scholar
  18. 18.
    Kripke, S.A.: Semantical analysis of modal logic i normal modal propositional calculi. Mathematical Logic Quaterly 9, 67–96 (1963)CrossRefzbMATHGoogle Scholar
  19. 19.
    Antova, L., Koch, C., Olteanu, D.: 10 worlds and beyond: efficient representation and processing of incomplete information. The VLDB Journal 18, 1021–1040 (2009)CrossRefGoogle Scholar
  20. 20.
    Lange, K.: Numerical analysis for statisticians. Springer, Heidelberg (1999)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anna Follmann
    • 1
  • Mario A. Nascimento
    • 2
  • Andreas Züfle
    • 1
  • Matthias Renz
    • 1
  • Peer Kröger
    • 1
  • Hans-Peter Kriegel
    • 1
  1. 1.Department of Computer ScienceLudwig-Maximilians-UniversitätGermany
  2. 2.University of AlbertaCanada

Personalised recommendations