Advertisement

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
Article

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.

Keywords

Internet of things Wireless sensor networks Compressed sensing 

Notes

Acknowledgements

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

References

  1. 1.
    Salim, A., Osamy, W.: Distributed multi chain compressive sensing based routing algorithm for wireless sensor networks. Wireless Netw. 21(4), 1379–1390 (2015)CrossRefGoogle Scholar
  2. 2.
    Rathore, P., Rao, A.S., Rajasegarar, S., Vanz, E., Gubbi, J., Palaniswami, M.: Real-time urban microclimate analysis using internet of things. IEEE Internet Things J. 99, 1 (2017)Google Scholar
  3. 3.
    Lalos, A.S., Antonopoulos, A., Kartsakli, E., Renzo, M.D.: Rlnc-aided cooperative compressed sensing for energy efficient vital signal telemonitoring. IEEE Trans. Wireless Commun. 14(7), 3685–3699 (2015)CrossRefGoogle Scholar
  4. 4.
    Mirabella, S., Oliveri, I.P., Ruffino, F., Maccarrone, G., Bella, S.D.: Low-cost chemiresistive sensor for volatile amines based on a 2d network of a zinc(ii) schiff-base complex. Appl. Phys. Lett. 109(14), 7315–7354 (2016)CrossRefGoogle Scholar
  5. 5.
    Vieira, R.G., Cunha, A.M.D., Camargo, A.P.D.: An energy management method of sensor nodes for environmental monitoring in amazonian basin. Wireless Netw. 20(3), 1–15 (2015)Google Scholar
  6. 6.
    Haghighat, J., Hamouda, W.: A power-efficient scheme for wireless sensor networks based on transmission of good bits and threshold optimization. IEEE Trans. Commun. 64(8), 3520–3533 (2016)CrossRefGoogle Scholar
  7. 7.
    Chen, S., Liu, J., Wang, K., Wu, M.: A hierarchical adaptive spatio-temporal data compression scheme for wireless sensor networks. Wireless Netw. 10, 1–10 (2017)Google Scholar
  8. 8.
    Shirvanimoghaddam, M., Li, Y., Vucetic, B., Yuan, J., Zhang, P.: Binary compressive sensing via analog fountain coding. IEEE Trans. Signal Process. 63(24), 6540–6552 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Phamila, A.V.Y., Amutha, R.: Energy-efficient low bit rate image compression in wavelet domain for wireless image sensor networks. Electron. Lett. 51(11), 824–826 (2015)CrossRefGoogle Scholar
  10. 10.
    Mei, Q., Hua, Q., Tong, B., Shi, Y., Chen, C., Huang, W.: A reversible and highly selective phosphorescent sensor for hg2+ based on iridium (iii) complex. Tetrahedron 71(49), 9366–9370 (2015)CrossRefGoogle Scholar
  11. 11.
    Xiao, S., Li, W., Jiang, H., et al.: Trajectroy prediction for target tracking using acoustic and image hybrid wireless multimedia sensors networks. Multimedia Tools Appl. (2017).  https://doi.org/10.1007/s11042-017-4846-z CrossRefGoogle Scholar
  12. 12.
    Xiao, S., Li, W., Shang, T.: Fuzzy logic based high speed data transmission algorithm of sensor networks for target tracking. J. Intell. Fuzzy Syst. 33(5), 2887–2893 (2017)CrossRefGoogle Scholar

Copyright information

© 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

Personalised recommendations