ABSTRACT
Compressive sensing (CS) is a new paradigm in signal processing and sampling theory. The chapter introduces the mathematical foundations of this novel theory and explores its applications in wireless sensor networks (WSNs). CS is an important achievement in sampling theory and signal processing. The CS theory has found applications in many scientific and industrial areas like magnetic resonance imaging, multimedia, genetics, and WSNs. In every area, there are customized algorithms and techniques that try to improve the CS performance for a specific data acquisition and recovery technique. Moreover, transform coding and CS are strongly connected topics and having a good knowledge of transform coding helps in better understanding of CS and its advantages. The sensor recordings are transmitted periodically over a communication channel. Depending on the technology used for a transferring data, this process entails different levels of transmission cost.
