Compressed sensing in wireless sensor networks under complex conditions of Internet of things

Shuo Xiao, Tianxu Li, Yan Yan, Jiayu Zhuang

Abstract: Based on the analysis of the traditional compressed sensing method, the problem of multi signal processing in the Internet of things is discussed in detail. A class of distributed compressive sensing methods based on time correlation is proposed. By means of time correlation, a linear regression method is used to segment the experimental signals. On this basis, the joint sparse model of distributed compressed sensing is improved, and a compression matrix is designed to extract the linear fitting part of the signal. Then, the adaptive compressed sensing is used to compress the signal processed by the compressed matrix, thus forming a complete new scheme of compressed sensing signal processing.