Shape-based clustering in wireless sensor networks
I. O. Okeke, Fabio Verdicchio · 2017
A low-complexity algorithm is presented that clusters sensor nodes based on similarity in the sensed signals. This feature makes it an enabler for distributed detection of events that are impossible to identify using information available to a single node. The algorithm does not require system training prior to deployment nor does it assume statistical knowledge of the signal. Experimental results confirm that clusters produced by our algorithm match signal patterns more closely than those formed by a comparatively simple algorithm that minimizes Euclidean distance between signals.