Efficient Data Monitoring in Sensor Networks Using Spatial Correlation

  • Jun-Ki MinEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 274)


In order to reduce r the energy consumption of sensors, we present an approximate data gathering technique, called CMOS, based on the Kalman filter. The goal of CMOS is to efficiently obtain the sensor readings within a certain error bound. In our approach, spatially close sensors are grouped as a cluster. Since a cluster header generates approximate readings of member nodes, a user query can be answered efficiently using the cluster headers. Our simulation results with synthetic data demonstrate the efficiency and accuracy of our proposed technique.


sensor network data monitoring Kalman filter 


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  1. 1.
    Heinzelman, W.B., Chandrakasan, A.P., Balakrishnan, H.: An application-specific protocolarchitecture for wireless microsensor networks. IEEE Transactions on Wireless Communications 1(4), 660–670 (2002)CrossRefGoogle Scholar
  2. 2.
    Jain, A., Chang, E.Y., Wang, Y.-F.: Adaptive stream resource management using kalmanfilters. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, pp. 11–22 (June 2004)Google Scholar
  3. 3.
    Kalman, R.E.: A new approach to linear filtering and prediction problem. Transactions of ASME Journal of Basic Engineering 82, 34–45 (1960)Google Scholar
  4. 4.
    Kotidis, Y.: Snapshot queries: Towards data-centric sensor networks. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE), pp. 131–142 (April 2005)Google Scholar
  5. 5.
    Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: Tag: A tiny aggregation service forad-hoc sensor networks. In: 5th Symposium on Operating System Design and Implementation (OSDI) (December 2002)Google Scholar
  6. 6.
    Min, J.-K., Chung, C.-W.: Edges: Efficient data gathering in sensor networks using temporaland spatial correlations. Journal of Systems and Software 25(5), 933–944 (2010)Google Scholar
  7. 7.
    Stern, M., Bohm, K., Buchmann, E.: Processing continuous join queries in sensor networks: a filtering approach. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pp. 267–278 (2010)Google Scholar
  8. 8.
    Tulone, D., Madden, S.: PAQ: Time series forecasting for approximate query answering in sensor networks. In: Römer, K., Karl, H., Mattern, F. (eds.) EWSN 2006. LNCS, vol. 3868, pp. 21–37. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringKorea University of Technology and EducationChungNamRepublic of Korea

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