Skip to main content

Advertisement

Springer Nature Link
Log in

A Novel Regression Based Clustering Technique for Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The objective of this paper is to develop a new adaptive iterative linear regression based clustering algorithm for wireless sensor network. According to this, the initial cluster is classified horizontally and vertically in parallel, each resulting in two sub-clusters. Of these two, the best is selected based on the proposed similarity index and with this selected cluster as reference, iteration continues until the convergence criterion ‘Delta’ is met. The similarity index is designed based on the intra cluster similarity and inter cluster dissimilarity. Delta is the difference between the similarity index of the current iteration and the previous iteration. The proposal is implemented in MATLAB and simulations are carried out under different network scenarios. The cluster quality is evaluated through external and internal indices using the Cluster Validity Analysis Platform tool. The cluster obtained by the proposal is studied and its quality is compared with the well-established k-means and hierarchical clustering. The performance indices confirm the supremacy of the proposal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37
Fig. 38
Fig. 39
Fig. 40
Fig. 41

Similar content being viewed by others

References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38, 393–422. doi:10.1016/S1389-1286(01)00302-4.

    Article  Google Scholar 

  2. Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications. doi:10.1109/MWC.2004.1368893.

    Google Scholar 

  3. Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: A survey. IEEE Transactions on Wireless Communication, 11, 6–28. doi:10.1109/MWC.2004.1368893.

    Article  Google Scholar 

  4. Hemavathi, N., & Sudha, S. (2014). A fuzzy based predictive cluster head selection scheme for wireless sensor networks. In The proceedings of 8th International Conference on Sensing Technology & International Journal on Smart Sensing and Intelligent Systems, pp. 560–567.

  5. Hemavathi, N., & Sudha, S. (2015). Hardware realization of fuzzy based cluster head selection in wireless sensor networks. In International conference on electronic design, computer networks & automated verification (2015).

  6. Sasikumar, P., Khara, S. (2012). K-Means clustering in wireless sensor network. In Fourth international conference on computational intelligence and communication networks, pp 140–144. doi:10.1109/CICN.2012.136.

  7. Amini, A., Wah, T. Y., & Saboohi, H. (2014). Density-based data streams clustering algorithms: A survey. Journal of Computer Science and Technology. doi:10.1007/s11390-013-1416-3.

    Google Scholar 

  8. Kumar, G., Mehra, H., Seth, A. R., Radhakrishnan, P., Hemavathi, N., & Sudha, S. (2014). An hybrid clustering algorithm for optimal clusters in wireless sensor networks. In IEEE students’ conference on electrical, electronics and computer science, pp. 1–6. doi:10.1109/SCEECS.2014.6804442.

  9. He, H., & Qina, Z. (2010). K-Hyperplane-based neural network for non-linear regression. In Proceedings of 9th IEEE international conference on cognitive informatics, cognitive informatics (ICCI), pp. 783–787. doi:10.1109/COGINF.2010.5599808.

  10. Shi, Y., Zhang, K., Liu, B., & Cui, L. (2011). A new QoS prediction approach based on user clustering and regression algorithms. IEEE International Conference on Web Services. doi:10.1109/ICWS.2011.95.

    Google Scholar 

  11. Ollos, G., & Vida, R. (2009). Adaptive regression algorithm for distributed dynamic clustering in wireless sensor networks. IEEE Conference on Wireless Days (WD). doi:10.1109/WD.2009.5449687.

    Google Scholar 

  12. Zhang, B. (2003). Regression clustering. In Proceedings of the third IEEE international conference on data mining (Icdm’03), pp. 451–458. doi:10.1109/ICDM.2003.1250952.

  13. Liu, Z., Xing, W., Zeng, B., Wang, Y., & Lu, D. (2013). Distributed spatial correlation-based clustering for approximate data collection in WSNS. In IEEE 27th international conference on advanced information networking and applications, pp. 56–63. doi:10.1109/AINA.2013.26.

  14. Zhang, B., Liu, Y., He, J., & Zou, Z. (2013). An energy efficient sampling method through joint linear regression and compressive sensing. In Fourth international conference on intelligent control and information processing (ICICIP), pp. 447–450. doi:10.1109/ICICIP.2013.6568115.

  15. Weng, J., & Hwang, W.-S. (2001). Incremental hierarchical discriminant regression for online image classification. In IEEE sixth international conference on document analysis and recognition, pp. 476–480. doi:10.1109/ICDAR.2001.953835.

  16. Shakibian, H., & Charkari, N. M. (2010). MCW: A new weighting method for linear combination of regressors in WSNS. In 5th international symposium on telecommunications (IST’2010), pp. 231–236. doi:10.1109/ISTEL.2010.5734029.

  17. Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2002). Clustering validity checking methods: Part-I. ACM Sigmod Record, 31, 2.

    Google Scholar 

  18. Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2002). Clustering validity checking methods: Part-II. ACM Sigmod Record, 31, 3.

    Article  MATH  Google Scholar 

  19. Xu, R., Xu, J., & Wunsch, D. C. (2012). A comparison study of validity indices on swarm-intelligence-based clustering. IEEE Transactions on Systems, Man and Cybernetics—Part B Cybernetics, 42, 1243–1256. doi:10.1109/TSMCB.2012.2188509.

    Google Scholar 

  20. Qiao, H., & Edwards, B. (2009). A data clustering tool with cluster validity indices. IEEE International Conference on Computing, Engineering and Information. doi:10.1109/ICC.2009.76.

    Google Scholar 

  21. Maulik, U., & Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 1650–1654. doi:10.1109/TPAMI.2002.1114856.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, 620015, India

    N. Hemavathi &  S. Sudha

Authors
  1. N. Hemavathi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. S. Sudha
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to S. Sudha.

Appendix

Appendix

figure c
figure d
figure e
Table 3 Nomenclature
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

N. Hemavathi, S. Sudha A Novel Regression Based Clustering Technique for Wireless Sensor Networks. Wireless Pers Commun 88, 985–1013 (2016). https://doi.org/10.1007/s11277-016-3226-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3226-8

Keywords