Sparse Recovery Optimization in Wireless Sensor Networks with a Sub-Nyquist Sampling Rate

作     者：Brunelli, Davide Caione, Carlo 

作者机构：Univ Trento I-38122 Trento Italy Univ Bologna I-40136 Bologna Italy 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2015年第15卷第7期

页      面：16654-16673页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：projects GreenDataNet - EU 7th Framework Programme 

主　　题：compressed sensing wireless sensor networks distributed compressed sensing embedded software low-power electronics 

摘      要：Compressive sensing (CS) is a new technology in digital signal processing capable of high-resolution capture of physical signals from few measurements, which promises impressive improvements in the field of wireless sensor networks (WSNs). In this work, we extensively investigate the effectiveness of compressive sensing (CS) when real COTSresource-constrained sensor nodes are used for compression, evaluating how the different parameters can affect the energy consumption and the lifetime of the device. Using data from a real dataset, we compare an implementation of CS using dense encoding matrices, where samples are gathered at a Nyquist rate, with the reconstruction of signals sampled at a sub-Nyquist rate. The quality of recovery is addressed, and several algorithms are used for reconstruction exploiting the intra- and inter-signal correlation structures. We finally define an optimal under-sampling ratio and reconstruction algorithm capable of achieving the best reconstruction at the minimum energy spent for the compression. The results are verified against a set of different kinds of sensors on several nodes used for environmental monitoring.