Compressive Sensing and Its Application in Wireless Sensor Networks

• Published in: Intelligent Sensor Networks pp. 351 - 378
• Main Authors: Choi, Jae-Gun, Park, Sang-Jun, Lee, Heung-No
• Format: Book Chapter
• Language: English
• Published: CRC Press 2013
• Edition: 1
• Subjects: Separate Recovery; OMP Algorithm; Separate Reconstruction; Primal Dual Interior Point Method; Sensing Matrix; Sparse Set; Underdetermined System; Interior Point Method; Wireless Sensor Nodes; Recovery Algorithms; Joint Reconstruction; Sensor Networks; Support Set; Sensor Nodes; Compressive Sensing; Sparse Signal; SNR Increase; Joint Recovery; WSN; OMP; Sensor Network; Cognitive Wireless Sensor Networks; Sensors Increases; Exact Reconstruction; Gradient Type Algorithm
• ISBN: 1138199745; 9781439892817; 1439892814; 9781138199743
• DOI: 10.1201/b14300-17

This chapter discusses the application of a new compression technique called compressive sensing (CS) in wireless sensor networks. It focuses on the role of the fusion center, which is to recover the transmitted signals in the original waveforms for processing. CS is a signal acquisition and compression framework recently developed in the field of signal processing and information theory. In a conventional communication system, an analog-to-digital converter based on the Shannon-Nyquist sampling theorem is used to convert analog signals to digital signals. The CS provides a direct method that acquires compressed samples without going through the intermediate stages of conventional compression. In contrast to the conventional schemes considered in the previous paragraph, the CS method aims to acquire compressed samples directly. The total volume of the independently compressed data is much larger than that of jointly compressed data. In addition, the CS provides several recovery routines that the original signal can be regenerated perfectly from the compressed samples.