Cluster Computing

, Volume 22, Supplement 6, pp 14145–14155 | Cite as

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

  • Shuo Xiao
  • Tianxu Li
  • Yan Yan
  • Jiayu ZhuangEmail author


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.


Internet of things Wireless sensor networks Compressed sensing 



This work was supported by the Fundamental Research Fund for The Central Universities (2015QNA39) and the National Natural Science Funds of China (51674245).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Sciences and TechnologyChina University of Mining & TechnologyXuzhouChina
  2. 2.Business DepartmentJiangsu Xuzhou Higher Vocational School of Economics & TradingXuzhouChina
  3. 3.Agricultural Information InstituteChinese Academy of Agricultural SciencesBeijingChina

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