Location Based Distributed Spectral Clustering for Wireless Sensor Networks

Abstract

A distributed spectral clustering algorithm to group sensors based on their location in a wireless sensor network (WSN) is proposed. For machine learning and data mining applications in WSN's, gathering data at a fusion center is vulnerable to attacks and creates data congestion. To avoid this, we propose a robust distributed clustering method without a fusion center. The algorithm combines distributed eigenvector computation and distributed K-means clustering. A distributed power iteration method is used to compute the eigenvector of the graph Laplacian. At steady state, all nodes converge to a value in the eigenvector of the algebraic connectivity of the graph Laplacian. Clustering is carried out on the eigenvector using a distributed K-means algorithm. Location information of the sensor is only used to establish the network topology and this information is not exchanged in the network. This algorithm works for any connected graph structure. Simulation results supporting the theory are also provided.