Overlapping Multi-hop Clustering for Wireless Sensor Networks

TL;DR

This paper introduces KOCA, a distributed algorithm for creating overlapping multi-hop clusters in wireless sensor networks, enhancing scalability, load balancing, and application-specific functionalities.

Contribution

It formulates a new overlapping multi-hop clustering problem and proposes KOCA, a scalable, randomized distributed algorithm that ensures connected, evenly-sized, overlapping clusters.

Findings

KOCA produces approximately equal-sized clusters.

KOCA achieves scalable clustering in constant time.

Overlapping clusters benefit various sensor network applications.

Abstract

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multi-hop clusters. Overlapping clusters are useful in many sensor network applications, including inter-cluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multi-hop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allows distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size.