An Entropic Approach to Data Aggregation with Divergence Measure Based Clustering in Sensor Network

  • Adwitiya Sinha
  • D. K. Lobiyal
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 192)


In wireless sensor network, data fusion is considered an essential part for preserving energy. Periodic data sampling leads to enormous collection of raw facts, the transmission of which would rapidly deplete the sensor power. In this paper, we have performed data aggregation on the basis of entropy of the sources. The entropy is computed from the local and global probability models. The models provide assistance in extracting high precision data from the sensor nodes. Further, we have proposed an energy efficient method for clustering the sensor nodes. Initially the sensors sensing same category of data are placed within a distinct cluster. The remaining unclustered sensors estimate their divergence with respect to the clustered neighbors and ultimately join the least-divergent cluster. The performance of our proposed methods is evaluated using ns-2 simulator in terms of entropy, aggregation cycles and energy utilization. The simulation results confirm the validity and efficiency of our approach.


Wireless sensor network node clustering Kullback-Leibler directed divergence measure Jeffrey’s symmetric divergence measure data aggregation entropy fuzzy-entropy local and global probability measure 


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  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless Sensor Networks: A Survey. J. of Computer Networks 38, 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Chitnis, L., Dobra, A., Ranka, S.: Aggregation Methods for Large-Scale Sensor Networks. ACM Transactions on Sensor Networks 4(2), article 9, 1–36 (2008)CrossRefGoogle Scholar
  3. 3.
    Castelluccia, C., Chan, A.C.-F., Mykletun, E., Tsudik, G.: Efficient and Provably Secure Aggregation of Encrypted Data in Wireless Sensor Networks. ACM Transactions on Sensor Networks 5(3), article 20, 1–36 (2009)CrossRefGoogle Scholar
  4. 4.
    Xiong, N., Svensson, P.: Multi-Sensor Management for Information Fusion: Issues and Approaches. Information Fusion 3, 163–186 (2002)CrossRefGoogle Scholar
  5. 5.
    Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: An Application-Specific Protocol Architectures for Wireless Microsensor Networks. IEEE Transactions on Wireless Communications 1(4), 660–670 (2002)CrossRefGoogle Scholar
  6. 6.
    Ghiasi, S., Srivastava, A., Yang, X.J., Sarrafzadeh, M.: Optimal Energy Aware Clustering in Sensor Networks. Special Issue: Special Section on Sensor Network Technology and Sensor Data Managment 2, 258–269 (2004)Google Scholar
  7. 7.
    Srinivasan, S.M., Azadmanesh, A.: Data Aggregation in Static Adhoc Networks. In: Third IEEE International Conference on Industrial and Information Systems, ICIIS, pp. 1–6 (2008)Google Scholar
  8. 8.
    Wang, X., Li, J.: Precision Constraint Data Aggregation for Dynamic Cluster-Based Wireless Sensor Networks. In: 5th International Conference on Mobile Ad-hoc and Sensor Network (MSN 2009), pp. 172–179 (2009)Google Scholar
  9. 9.
    Zhao, F., Shin, J., Reich, J.: Information-Driven Dynamic Sensor Collaboration. IEEE Signal Processing Magazine 19, 61–72 (2002)CrossRefGoogle Scholar
  10. 10.
    Commuri, S., Tadigotla, V.: Dynamic Data Aggregation in Wireless Sensor Networks. In: IEEE 22nd International Symposium on Intelligent Control, pp. 1–6 (2007)Google Scholar
  11. 11.
    Kong, L., Chen, Z., Yin, F.: Optimum Design of a Window Function Based on the Small-World Networks. In: IEEE International Conference on Granular Computing, p. 97 (2007)Google Scholar
  12. 12.
    Eguchi, S., Copus, J.: Interpreting Kullback–Leibler Divergence with the Neyman–Pearson Lemma. Journal of Multivariate Analysis 97, 2034–2040 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Chang, H., Yao, Y., Koschan, A., Abidi, B., Abidi, M.: Improving Face Recognition via Narrowband Spectral Range Selection Using Jeffrey Divergence. IEEE Transactions on Information Forensics and Security 4(1), 111–123 (2009)CrossRefGoogle Scholar
  14. 14.
    Duch, W.: Uncertainty of Data, Fuzzy Membership Functions, and Multi-layer Perceptrons. IEEE Transaction on Neural Networks 20, 1–12 (2004)Google Scholar
  15. 15.
    Mandal, S.N., Choudhury, J.P., De, D., Chaudhuri, S.R.B.: Roll of Membership Functions in Fuzzy Logic for Prediction of Shoot Length of Mustard Plant Based on Residual Analysis. World Academy of Science, Engineering and Technology 38, 378–384 (2008)Google Scholar
  16. 16.
    Fall, K., Varadhan, K.: The ns Manual. The VINT Project (2009)Google Scholar
  17. 17.
    Altman, E., Jemenez, T.: NS simulator for beginners (2003)Google Scholar
  18. 18.
    Greis, M.: Tutorial for Network Simulator (2009)Google Scholar
  19. 19.
    Younis, O., Fahmy, S.: Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach. IEEE INFOCOM, 1–12 (2004)Google Scholar
  20. 20.
    Anker, T., Bickson, D., Dolev, D., Hod, B.: Efficient clustering for improving network performance in wireless sensor networks. In: Verdone, R. (ed.) EWSN 2008. LNCS, vol. 4913, pp. 221–236. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Adwitiya Sinha
    • 1
  • D. K. Lobiyal
    • 1
  1. 1.Jawaharlal Nehru UniversityNew DelhiIndia

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