Tirza Routtenberg

The advance of wireless sensor networks (WSNs) in diverse applications has accelerated in recent years, making them crucial components in many spheres of modern life. From the engineering perspective, data collected from WSNs can be considered as high-dimensional signals with irregular and complex structures. In this chapter, we present graph signal processing (GSP) tools, suited to the treatment of signals over irregular domains, for the analysis of WSN data output. Specifically, we discuss fundamental concepts in GSP, including the graph Fourier transform (GFT) and Laplacian-based regularization, and more recent GSP methodologies, including smoothness validation, signal recovery, anomaly detection, and topology identification, in the context of WSN applications.