Abstract
Wireless Sensor Networks (WSNs) are widely employed to solve several problems in different domains like agriculture, monitoring, health care. An outlier detection method is considered as a task of classifying data that is available during training. Also, this method is considered as a very important step in construction of sensor network systems to ensure data grade for perfect decision making. So, this task helps to create a gainful approach based on Non-Negative Matrix Factorization (NMF) to find out if data is normal regular or an outlier. The latter has been used for nonlinear instance which can give a highest order statistics. Then, it has showed excellent performance with accounts for an attractive capability and has been extensively investigated. In this work, we propose a Non-Negative Matrix Factorization (NMF) as new method for an outlier detection to separate an outlier data from a regular pattern of point distribution. Using real data collected from Grand St-Bernard and Intel Berkeley in wireless sensor networks, our proposed work show a better performance in detecting outliers.