Compressive sensing for wireless sensor networks

Compressive sensing (CS) is a new paradigm in signal processing and sampling theory. In this chapter, we introduce the mathematical foundations of this novel theory and explore its applications in wireless sensor networks (WSNs). CS is an important achievement in sampling theory and signal processing. It is increasingly being implied in many areas like multimedia, machine learning, medical imaging, etc. We focus on the aspects of CS theory that has direct applications in WSNs. We also investigate the most well-known implementations of CS theory for data collection in WSNs. © 2013 by Taylor & Francis Group, LLC