  Wireless sensor networks usually comprise a large number of sensors
monitoring changes in variables. These changes in variables represent changes
in physical quantities. The changes can occur for various reasons; these
reasons are highlighted in this work. Outliers are unusual measurements.
Outliers are important; they are information-bearing occurrences. This work
seeks to identify them based on an approach presented in [1]. A critical review
of most previous works in this area has been presented in [2], and few more are
considered here just to set the stage. The main work can be described as this;
given a set of measurements from sensors that represent a normal situation, [1]
proceeds by first estimating the probability density function (pdf) of the set
using a data-split approach, then estimate the entropy of the set using the
arithmetic mean as an approximation for the expectation. The increase in
entropy that occurs when strange data is recorded is used to detect unusual
measurements in the test set depending on the desired confidence interval or
false alarm rate. The results presented in [1] have been confirmed for
different test signals such as the Gaussian, Beta, in one dimension and beta in
two dimensions, and a beta and uniform mixture distribution in two dimensions.
Finally, the method was confirmed on real data and the results are presented.
The major drawbacks of [1] were identified, and a method that seeks to mitigate
this using the Bhattacharyya distance is presented. This method detects more
subtle anomalies, especially the type that would pass as normal in [1].
Finally, recommendations for future research are presented: the subject of
interpretability, especially for subtle measurements, being the most elusive as
of today.
