  An experiment to study the entropy method for an anomaly detection system has
been performed. The study has been conducted using real data generated from the
distributed sensor networks at the Intel Berkeley Research Laboratory. The
experimental results were compared with the elliptical method and has been
analyzed in two dimensional data sets acquired from temperature and humidity
sensors across 52 micro controllers. Using the binary classification to
determine the upper and lower boundaries for each series of sensors, it has
been shown that the entropy method are able to detect more number of out
ranging sensor nodes than the elliptical methods. It can be argued that the
better result was mainly due to the lack of elliptical approach which is
requiring certain correlation between two sensor series, while in the entropy
approach each sensor series is treated independently. This is very important in
the current case where both sensor series are not correlated each other.
