A Distributed Clustering Algorithm for Dynamic Networks

TL;DR

This paper introduces a decentralized, mobility-adaptive clustering algorithm for dynamic networks that uses circulating tokens to form and maintain clusters with sizes above a threshold, ensuring local optimality and scalability.

Contribution

It presents a novel distributed clustering method that adapts to network mobility using token circulation, enabling scalable and locally optimal clustering.

Findings

Clusters maintain size above threshold m.

Algorithm converges after topological changes.

Clusters are locally optimal with maximal number.

Abstract

We propose an algorithm that builds and maintains clusters over a network subject to mobility. This algorithm is fully decentralized and makes all the different clusters grow concurrently. The algorithm uses circulating tokens that collect data and move according to a random walk traversal scheme. Their task consists in (i) creating a cluster with the nodes it discovers and (ii) managing the cluster expansion; all decisions affecting the cluster are taken only by a node that owns the token. The size of each cluster is maintained higher than $m$ nodes ($m$ is a parameter of the algorithm). The obtained clustering is locally optimal in the sense that, with only a local view of each clusters, it computes the largest possible number of clusters (\emph{ie} the sizes of the clusters are as close to $m$ as possible). This algorithm is designed as a decentralized control algorithm for large scale networks and is mobility-adaptive: after a series of topological changes, the algorithm converges to a clustering. This recomputation only affects nodes in clusters in which topological changes happened, and in adjacent clusters.