摘要：To improve data reliability, accuracy and to make effective and correct decisions using data collected from the wireless sensor network, it is necessary to detect the inconsistent data (outlier) caused by compromised or malfunctioning nodes. Data aggregation is augmented to eliminate the outlier data in the sensor network by multivariate analysis technique such as factor analysis and mahalanobis distance. Factor analysis is a way to fit a model to multivariate data to estimate the interdependence. In a factor analysis model, the measured variables depend on a smaller number of unobserved (latent) factors. The mahalanobis distance is used to determine the similarity of a set of values from an unknown samp1e to a set of values measured from a collection of known samples. Combined with factor analysis, Mahalanobis distance is extended to examine whether a given vector is an outlier from a model identified by factors based on factor analysis. In this paper to ensure accuracy during the aggregation process, factor analysis and mahalanobis distance methodologies are used and the data inconsistency is optimized. The performance graph shows that the Factor analysis and mahalanobis distance detects outlier better than the principal component analysis and subspace methods
