A sensor node, a sensor network and a method for autonomous decision-making in sensor networks

For providing an improved approach to employ machine learning techniques in a network of embedded sensor nodes for making decisions autonomously, such as detecting anomalous behavior in a sensor network, the invention proposes a method for classifying data generated by at least one sensor node (100, 211-218) of a sensor network (200) by statistical processing of said data based on linear discriminant analysis, wherein said data is represented by an n-dimensional test feature vector, comprising a learning phase (310-360) with the steps of defining at least two classes into which an n-dimensional feature vector may be classified, providing a set of labeled n-dimensional learning feature vectors, each of which is associated with one of said classes, and pre-processing of said set of labeled learning feature vectors, and an inference phase (410, 420) with the steps of calculating (410) for each class a linear discriminant function of the n-dimensional test feature vector representing the data to be classified, based on the results of said pre-processing, and assigning (420) the data to the class for which the calculated linear discriminant function yields the highest result. In particular, the method is capable of correlating different kinds of data in order to make decisions. The invention further proposes a sensor node (100) and a sensor network (200) adapted for performing the method.