Clustering-Aided Graph Signal Sampling and Reconstruction for Large-Scale Sensor Networks

Abstract

In this paper we develop a clustering-aided signal sampling and reconstruction method for data acquisition in large-scale sensor networks. Using the localization feature of a large network, we exploit the vertex-domain locality by the localized operator of each vertex on the graph, and develop a clustering method that sequentially selects cluster heads and their corresponding members by the use of the overlap factor of each vertex. On this basis, we apply greedy sampling set selection for each cluster in a distributed manner. By combining all local sampling sets, the global sampling set is selected and signals over the whole graph is then efficiently reconstructed. Simulation results over various large networks show that compared with existing sampling set selection methods, the proposed method can reduce the computational complexity while achieving acceptable reconstruction accuracy.