In-network adaptive cluster enumeration for distributed classification and labeling
Freweyni K. Teklehaymanot, Michael Muma, Jun Liu, Abdelhak M. Zoubir · 2016
A crucial first step for signal processing decentralized sensor networks with node-specific interests is to agree upon a common unique labeling of all observed sources in the network. The knowledge “who observes what” is required, e.g. in node-specific audio or video signal enhancement to form node clusters of common interest. Recently proposed in-network distributed adaptive classification and labeling algorithms assume knowledge on the number of objects (clusters), which is not necessarily available in real-world applications. Thus, we consider the problem of estimating the number of data-clusters in the distributed adaptive network set-up. We propose two distributed adaptive cluster enumeration methods. They combine the diffusion principle, where the nodes share information within their local neighborhood only (without fusion center), with the X-means and the PG-means cluster enumeration. Performance is evaluated via simulations and the applicability of the methods is illustrated using a distributed camera network where moving objects appear and disappear from the Line-of-Sight (LOS) and the number of clusters becomes time-varying.