ABSTRACT In Wireless Sensors Networks, the computational power and storage capacity is limited. Wireless Sensor Networks are operated in low power batteries, mostly not rechargeable. The amount of data processed is incremental in nature, due to deployment of various applications in Wireless Sensor Networks, thereby leading to high power consumption in the network. For effectively processing the data and reducing the power consumption the discrimination of noisy, redundant and outlier data has to be performed. In this paper we focus on data discrimination done at node and cluster level employing Data Mining Techniques. We propose an algorithm to collect data values both at node and cluster level and finding the principal component using PCA techniques and removing outliers resulting in error free data. Finally a comparison is made with the Statistical and Bucket-width outlier detection algorithm where the efficiency is improved to an extent.
Cite this paper
B. Sabarish and S. Shanmugapriya, "Improved Data Discrimination in Wireless Sensor Networks," Wireless Sensor Network, Vol. 4 No. 4, 2012, pp. 117-119. doi: 10.4236/wsn.2012.44016.
 A. Kathirvel and R. Srinivasan, “Self Umpiring System for Security in Wireless Mobile Ad Hoc Network,” Wireless Sensor Network, Vol. 2, No. 3, 2010, pp. 264-266.
 V. Jha and O. V. S. Yadav, “Outlier Detection Techniques and Cleaning of Data for Wireless Sensor Networks: A Survey,” International Journal of Computer Science and Technology, Vol. 3, No. 1, 2012, pp. 45-49.
 L. Deri, S. Suin and G. Maselli, “Design and Implementation of an Anomaly Detection System: An Empirical Approach,” TERENA Networking Conference in Associa- tion with the CARNET Users’ Conference, Zagreb, 19-22 May 2003, pp. 1-20.
 Y. K. Jain and S. S. Patil, “Design and Implementation of Anomalies Detection System Using IP Gray Space Analysis”, International Conference on Future Networks, Bangkok, 7-9 March 2009, pp. 203-207.
 D.-I. Curiac, O. Banias, F. Dragan, C. Volosencu and O. Dranga, “Malicious Node Detection in Wireless Sensor Networks Using an Autoregression Technique” Third International Conference on Networking and Services, Athens, 19-25 June 2007, p. 83. doi:10.1109/ICNS.2007.79
 M. Walchli and T. Braun, “Efficient Signal Processing and Anomaly Detection in Wireless Sensor Networks”, Applications of Evo-lutionary Computing, Vol. 5484, 2009, pp. 81-86. doi:10.1007/978-3-642-01129-0_9
 S. Rajasegarar, C. Leckie, M. Palaniswami and J. C. Bezdek, “Distributed Anomaly Detection in Wireless Sensor Networks”, 10th IEEE Singapore International Conference on Communication Systems, Singapore, October 2006, pp. 1-5. doi:10.1109/ICCS.2006.301508
 E. Parvar, M.-R. Yazdani, N. EffatParvar, M. Dadlani and A. Khonsari, “Improved Algorithms for Leader Election in Distributed Systems”, 2nd International Conference on Computer Engineering and Technology (ICCET), 16-18 April 2010, pp. 6-10, doi:10.1109/ICCET.2010.5485357
 X. N. Cui, Q. Li, B. H. Zhao, “Data Discrimination in Fault-Prone Sensor Networks”, Wireless Sensor Network, Vol. 2, No. 4, 2010, pp. 285-292.
 K. Khedo, R. Doomun and S. Aucharuz, “READA: Redundancy Elimination for Accurate Data Aggregation in Wireless Sensor Networks,” Wireless Sensor Network, Vol. 2, No. 4, 2010, pp. 302-308.
 A. Weingessel and K. Hornik, “Local PCA Algorithms,” IEEE Transactions on Neural Networks, Vol. 11, No. 6, 2000, pp. 1242-1250. doi:10.1109/72.883408
 D.-A. Le Borgne, S. Ray-baud and G. Bontempi, “Distributed Principal Component Analysis for Wireless Sensor Networks,” Sensors, Vol. 8, No. 8, 2008, pp. 4821-4850.