Abstract
We consider the problem of estimating the clustering of nodes in wireless sensor networks (WSNs). A solution to this problem is proposed, which uses Least Squares Support Vector Machines (LS-SVM). Using mixtures of kernels and the image energy distribution of the sensor field surface, we have been solved the clustering problem in WSNs. Some computer experiments for the simulated sensor fields are carried out. Through comparing with classical clustering scheme we state that LS-SVM method has a better improvement in clustering accuracy in these networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless Sensor Networks. Computer Networks 38, 393–422 (2002)
Baker, D.J., Ephremides, A.: The Architectural Organization of a Mobile Radio Network via a Distributed Algorithm. IEEE Trans. on Communications 29(11), 1694–1701 (1981)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining 2(2), 121–167 (1998)
Chatterjee, M., Das, S., Turgut, D.: WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks. Cluster Computing Journal 5, 193–204 (2002)
Cortes, C., Vapnik, V.N.: Support Vector Networks. Machine Learning 20, 273–297 (1995)
Heinzelman, W.B., Chandrakasan, A., Balakrishnan, H.: Energy-Efficient Communication Protocol for Wireless Microsensor Networks. In: Proc. of the 33rd Hawaii International Conference on System Science, January 2000, pp. 174–185 (2000)
Heinzelman, W.B., Chandrakasan, A.P., Balakrishnan, H.: An Application-Specific Protocol Architecture for Wireless Microsensor Networks. IEEE Trans. on Wireless Networking 1(4), 660–670 (2002)
Karl, H., Willig, A.: Protocols and Architectures for Wireless Sensor Networks. John Wiley and Sons, Hoboken (2005)
Krishnan, R., Starobinski, D.: Efficient Clustering Algorithms for Self-organizing Wireless Sensor Networks. Ad Hoc Networks 4, 36–50 (2006)
Pelckmans, K., Suykens, J.A.K., van Gestel, T., de Brabanter, J., Lukas, L., Hamer, B., de Moor, B., Vandewalle, J.: LS-SVMLab Toolbox User’s Guide, ver. 1.5, http://www.esat.kuleuven.ac.be/be/sista/lssvmlab
Ruping, S.: Incremental Learning with Support Vector Machines. In: Proc. IEEE Int. Conf. on Data Mining, San Jose, CA, USA, November 2001, pp. 641–642 (2001)
Safari, M., Harandi, M.T., Araabi, B.N.: A SVM-based Method for Face Recognition Using a Wavelet PCA Representation of Faces. In: Int. Conf. on Image Processing (ICIP), pp. 853–856. IEEE Press, Los Alamitos (2004)
Smits, G.F., Jprdan, E.M.: Improved SVM Regression Using Mixtures of Kernels. In: Proc. of IJCNN 2002 on Neural Networks, vol. 3, pp. 2785–2790 (2002)
Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. Neural Processing Letters 9(3), 293–300 (1999)
Suykens, J.A.K., Vandewalle, J., De Moor, B.: Optimal Control by Least Squares Support Vector Machines. Neural Networks 14(1), 23–35 (2001)
Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewolle, J.: Least Squares Support Vector Machines. World Scientific, New Jersey (2002)
Tang, Sh., Li, W.: QoS Supporting and Optimal Energy Allocation for a Cluster Based Wireless Sensor Network. Computer Communications 29, 2569–2577 (2006)
Vapnik, V., Golowich, S., Smola, A.: Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing. In: Mozer, M., Jordan, M., Petsche, T. (eds.) Advances in Neural Information Processing Systems, vol. 9, pp. 281–287. MIT Press, Cambridge (1997)
Xie-Jian-hong: LS-SVM Method Applied to Detect Damage for Piezoelectric Smart Structures. Chinese Journal of Sensors and Actuators 20(1), 164–167 (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Martyna, J. (2008). An Application of LS-SVM Method for Clustering in Wireless Sensor Networks. In: Nguyen, N.T., Katarzyniak, R. (eds) New Challenges in Applied Intelligence Technologies. Studies in Computational Intelligence, vol 134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79355-7_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-79355-7_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79354-0
Online ISBN: 978-3-540-79355-7
eBook Packages: EngineeringEngineering (R0)