期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：59
期号：3
出版社：Journal of Theoretical and Applied
摘要：Inconsistent data caused by compromised nodes in Wireless Sensor Networks can be detached to improve data reliability, accuracy and to make effective and correct decisions. Multivariate Outliers normally describe the data behavior abnormality. Data aggregation is frequently used for the reduction of communication overhead and energy expenditure of sensor nodes during the process of data collection in Wireless Sensor Networks and also to improve the lifetime of the WSN. For the delivery of accuracy in base station, the outlier detection protocol must be incorporated with secure data aggregation. Aggregation will also try to increase the circle of knowledge and the level of accuracy. In this paper we use multivariate data analysis technique, data to handle outlier in correlated variables. To achieve the reliability and accuracy, a two phase algorithm is proposed. First, to build up a well conditioned PCA model for fault detection. Second, we use various statistical techniques to determine similarity between the sensed data against the real data set. We have evaluated our algorithm based on synthetic and real data injected with synthetic faults collected from a WSN. Our results concludes that the proposed algorithm achieves high true alarm rate and low false alarm rate and outperforms all the existing methods in terms of data accuracy and reliability.
