Systolic Query Processing for Aggregation in Sensor Networks

  • Suraj Pandey
  • Ho Seok Kim
  • Sang Hun Eo
  • Hae Young Bae
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4159)


Pipelining the messaging between sensor nodes increases the overall throughput of the querying system, however at the cost of extra communication. But for long running queries, the messages communicated in pipelined architecture are even less than the normal count of messages in any query processing methodology in sensor networks, as also pointed out in previous work. In this paper we device a novel methodology to process aggregation queries in sensor networks by using the systolic architecture. We explicitly define and stipulate the use of systolic message communication as aggregation query processing technique to yield increased response time with the saving of energy by reduced message communication when considering long running queries. We show through simulation the two-fold gain using the proposed technique as compared to methods without pipelining.


Sensor Network Sensor Node Cluster Head Processing Element Query Processing 
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.


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  1. 1.
    Hill, J.L., Culler, D.E.: Mica: A Wireless Platform for Deeply Embedded Networks. IEEE Micro 22(6), 12–24 (2002)CrossRefGoogle Scholar
  2. 2.
    Bajaj, S., et al.: Improving simulation for network research. Tech. Report 99-702b, University of Southern California, March 1999 (revised, September 1999)Google Scholar
  3. 3.
    Estrin, D., Handley, M., Heidemann, J., McCanne, S., Xu, Y., Yu, H.: Network visualization with the Nam, VINT network animator. IEEE Computer 11, 63–68 (2000)Google Scholar
  4. 4.
    Schurgers, C.: Optimizing Sensor Networks in the Energy-Latency-Density Design Space. IEEE Trans. on Mobile Computing 1(1), 70–80 (2002)CrossRefGoogle Scholar
  5. 5.
    Madden, S., Franklin, M.J., Hellerstein, J., Hong, W.: TAG: a Tiny AGgregation Service for Ad-Hoc Sensor Networks. In: Proc. of 5th Annual Symposium on Operating Systems Design and Implementation (OSDI) (2002)Google Scholar
  6. 6.
    Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: a scalable and robust communication paradigm for sensor networks. In: Proceedings of the 6th annual ACM/IEEE international conference on mobile computing and networking, Boston, MA, USA, pp. 56–67 (2000)Google Scholar
  7. 7.
    Kung, H.T., Leiserson, C.E.: Systolic arrays for VLSI. In: Sparse Matrix Proceedings, SIAM, Philadelphia (1979)Google Scholar
  8. 8.
    Chen, B., Jamieson, K., Balakrishnan, H., Morris, R.: Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks. In: Proc. of MobiCom (July 2001)Google Scholar
  9. 9.
    Madden, S.R., Franklin, M.J., Hellerstein, J.M.: TinyDB: An Acquisitional Query Processing System for Sensor Networks. ACM Transactions on Database Systems, 121–173 (March 2005)Google Scholar
  10. 10.
    Yao, Y., Gehrke, J.: The Cougar Approach to In-Network Query Processing in Sensor Networks. In: SIGMOD (2002)Google Scholar
  11. 11.
    Zhao, J., Govindan, R., Estrin, D.: Computing Aggregates for Monitoring Wireless Sensor Networks. In: The First IEEE Intl. Workshop on Sensor Network Protocols and Applications (SNPA) (2003)Google Scholar
  12. 12.
    Przydatek, B., Song, D., Perrig, A.: Secure Information Aggregation in Sensor Networks. In: Proc. of the First ACM Conf. on Embedded Networked Systems (SenSys) (2003)Google Scholar
  13. 13.
    Considine, J., Li, F., Kollios, G., Byers, J.: Approximate Aggregation Techniques for Sensor Databases. In: Proc. of the 20th Intl. Conf. on Data Engineering (2004)Google Scholar
  14. 14.
    Heidemann, J., Silva, F., Intanagonwiwat, C., Govindan, R., Estrin, D., Ganesan, D.: Building Efficient Wireless Sensor Networks with Low-level Naming. In: SOAP (2001)Google Scholar
  15. 15.
    Greenstein, B., Estrin, D., Govindan, R., Ratnasamy, S., Shenker, S.: DIFS: A Distributed Index for Features in Sensor Networks. In: Proc. 1st IEEE International Workshop on Sensor Network Protocols and Applications, Anchorage, AK (2003)Google Scholar
  16. 16.
    Eo, S.H., Pandey, S., Park, S.-Y., Bae, H.-Y.: FDSI-Tree: A Fully Distributed Spatial Index Tree for Efficient & Power-Aware Range Queries in Sensor Networks. In: SOFSEM 2006, pp. 254–261 (2006)Google Scholar
  17. 17.
    Ratnasamy, S., Karp, B., Yin, L., Yu, F., Estrin, D., Govindan, R., Shenker, S.: GHT: A Geographic Hash Table for Data-Centric Storage in SensorNets. In: Proc. of 1st ACM WSNA (September 2002)Google Scholar
  18. 18.
    Lyall, A., et al.: Implementation of Inexact String Matching on the ICL DAP. In: Feilmeier, et al. (eds.) Parallel Computing 1985, North-Holland, Amsterdam (1986)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Suraj Pandey
    • 1
  • Ho Seok Kim
    • 1
  • Sang Hun Eo
    • 1
  • Hae Young Bae
    • 1
  1. 1.Department of Computer Science and Information EngineeringInha UniversityIncheonKorea

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