Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks

TL;DR

This paper introduces Gravitational Clustering, a novel distributed algorithm that adaptively estimates the number of clusters in sensor networks by simulating gravitational forces among feature vectors, enabling robust, cooperative signal processing.

Contribution

It presents a new gravitational-based clustering method that adaptively determines the number of clusters in distributed networks, unlike previous methods requiring known cluster counts.

Findings

Robustness against outliers demonstrated

Convergence properties established

Suitable for real-world distributed sensor applications

Abstract

Distributed signal processing for wireless sensor networks enables that different devices cooperate to solve different signal processing tasks. A crucial first step is to answer the question: who observes what? Recently, several distributed algorithms have been proposed, which frame the signal/object labelling problem in terms of cluster analysis after extracting source-specific features, however, the number of clusters is assumed to be known. We propose a new method called Gravitational Clustering (GC) to adaptively estimate the time-varying number of clusters based on a set of feature vectors. The key idea is to exploit the physical principle of gravitational force between mass units: streaming-in feature vectors are considered as mass units of fixed position in the feature space, around which mobile mass units are injected at each time instant. The cluster enumeration exploits the fact that the highest attraction on the mobile mass units is exerted by regions with a high density of feature vectors, i.e., gravitational clusters. By sharing estimates among neighboring nodes via a diffusion-adaptation scheme, cooperative and distributed cluster enumeration is achieved. Numerical experiments concerning robustness against outliers, convergence and computational complexity are conducted. The application in a distributed cooperative multi-view camera network illustrates the applicability to real-world problems.