Approximate Top-k Queries in Sensor Networks

(Extended Abstract)
  • Boaz Patt-Shamir
  • Allon Shafrir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4056)


We consider a distributed system where each node has a local count for each item (similar to elections where nodes are ballot boxes and items are candidates). A top-k query in such a system asks which are the k items whose sum of counts, across all nodes in the system, is the largest. In this paper we present a Monte-Carlo algorithm that outputs, with high probability, a set of k candidates which approximates the top-k items. The algorithm is motivated by sensor networks in that it focuses on reducing the individual communication complexity. In contrast to previous algorithms, the communication complexity depends only on the global scores and not on the partition of scores among nodes. If the number of nodes is large, our algorithm dramatically reduces the communication complexity when compared with deterministic algorithms. We show that the complexity of our algorithm is close to a lower bound on the cell-probe complexity of any non-interactive top-k approximation algorithm. We show that for some natural global distributions (such as the Geometric or Zipf distributions), our algorithm needs only polylogarithmic number of communication bits per node.


Sensor Network Communication Cost Communication Complexity Deterministic Algorithm Star Topology 
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.
    Attiya, H., Welch, J.: Distributed Algorithms. McGraw-Hill Publishing Company, UK (1998)Google Scholar
  2. 2.
    Babcock, B., Olston, C.: Distributed top-k monitoring. In: Proc. 2003 ACM SIGMOD (2003)Google Scholar
  3. 3.
    Bak, P.: How Nature Works: The science of self-organized criticality. Springer, New York (1996)zbMATHGoogle Scholar
  4. 4.
    Balke, W.-T., Nejdl, W., Siberski, W., Thaden, U.: Progressive distributed top k retrieval in peer-to-peer networks. In: Proc. 21st Int. Conf. on Data Engineering (2005)Google Scholar
  5. 5.
    Bruno, N., Gravano, L., Marian, A.: Evaluating top-k queries over web-accessible databases. In: Proc. 18th Int. Conf. on Data Engineering (2002)Google Scholar
  6. 6.
    Cao, P., Wang, Z.: Efficient top-k query calculation in distributed networks. In: Proc. 23rd Ann. ACM Symp. on Principles of Distributed Computing (2004)Google Scholar
  7. 7.
    Considine, J., Li, F., Kollios, G., Byers, J.: Approximate aggregation techniques for sensor databases (April, 2004)Google Scholar
  8. 8.
    Cormode, G., Garofalakis, M.N., Muthukrishnan, S., Rastogi, R.: Holistic aggregates in a networked world: Distributed tracking of approximate quantiles. In: Proc. 2005 ACM SIGMOD (2005)Google Scholar
  9. 9.
    Dagum, P., Karp, R.M., Luby, M., Ross, S.: An optimal algorithm for Monte Carlo estimation. SIAM J. Comput. 29(5) (2000)Google Scholar
  10. 10.
    Durand, M., Flajolet, P.: Loglog counting of large cardinalities (extended abstract). In: Algorithms: ESA 11th Ann. European Symp. (2003)Google Scholar
  11. 11.
    Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: Proc. 20th ACM Symp. on Principles of Database Systems (2001)Google Scholar
  12. 12.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: Proc. SIGCOMM 1999, ACM Press, New York (1999)Google Scholar
  13. 13.
    Fredman, M., Saks, M.: The cell probe complexity of dynamic data structures. In: Proceedings of the 21st Annual ACM Symposium on Theory of Computing (May 1989)Google Scholar
  14. 14.
    Greenwald, M., Khanna, S.: Power-conserving computation of order-statistics over sensor networks. In: Proc. 23rd ACM Symp. on Principles of Database Systems (2004)Google Scholar
  15. 15.
    Lynch, N.: Distributed Algorithms. Morgan Kaufmann, San Mateo (1995)Google Scholar
  16. 16.
    Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: The design of an acquisitional query processor for sensor networks. In: Proc. ACM SIGMOD (2003)Google Scholar
  17. 17.
    Michel, S., Triantafillou, P., Weikum, G.: Klee: A framework for distributed top-k query algorithms. In: Proc. 31st Int. Conf. on Very Large Data Bases (2005)Google Scholar
  18. 18.
    Nath, S., Gibbons, P.B., Seshan, S., Anderson, Z.R.: Synopsis diffusion for robust aggregation in sensor networks. In: SenSys 2004: Proc. 2nd international conference on Embedded networked sensor systems (2004)Google Scholar
  19. 19.
    Panconesi, A., Srinivasan, A.: Fast randomized algorithms for distributed edge coloring (extended abstract). In: Proc. 11th Ann. ACM Symp. on Principles of Distributed Computing (1992)Google Scholar
  20. 20.
    Patt-Shamir, B.: A note on efficient aggregate queries in sensor networks. In: Proc. 23rd Ann. ACM Symp. on Principles of Distributed Computing (2004)Google Scholar
  21. 21.
    Silberstein, A., Braynard, R., Ellis, C., Munagala, K., Yang, J.: A sampling-based approach to optimizing top-k queries in sensor networks. In: Proc. 22nd Int. Conf. on Data Engineering (2006)Google Scholar
  22. 22.
    Warneke, B.: Miniaturizing sensor networks with mems. In: Ilyas, M., Mahgoub, I. (eds.) Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, CRC Press, Boca Raton (2004)Google Scholar
  23. 23.
    Yao, A.C.-C.: Should tables be sorted? J. ACM 28(3) (1981)Google Scholar
  24. 24.
    Yao, Y., Gehrke, J.: The Cougar approach to in-network query processing in sensor networks. ACM SIGMOD Record 31(3), 9–18 (2002)CrossRefGoogle Scholar
  25. 25.
    Zeinalipour-Yazti, D., Vagena, Z., Gunopulos, D., Kalogeraki, V., Tsotras, V., Vlachos, M., Koudas, N., Srivastava, D.: The threshold join algorithm for top-k queries in distributed sensor networks. In: Proc. 2nd Int. Workshop on Data Management for Sensor Networks (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Boaz Patt-Shamir
    • 1
  • Allon Shafrir
    • 1
  1. 1.Dept. of Electrical EngineeringTel Aviv UniversityTel AvivIsrael

Personalised recommendations