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Optimizing Compressive Sensing in the Internet of Things

  • Guoyang Chen
  • Hao Yang
  • Liusheng Huang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 143)

Abstract

In order to save cost of sensors in the process of transmitting information and gathering data, Compressive Sensing(CS), as a novel and effective signal transform technology, has been used gradually in the Internet of Things(IOT). This paper presents an optimizing method of CS in real applications of IOT. Compared to the traditional CS techniques that the sparsity of the signal need to be known, this algorithm can effective solve the actual issue that a lot of signals’ sparsities usually could not be known in advance. According to experiments in the cases of both random distribution and Gauss distribution of signals, our algorithm is proved to be effective. Especially, when the amount of the sampling is 30 (the dimension of the data is 256) and the sparsity is unknown, the relative error is only 1.5%.

Keywords

Compressive Sensing the Internet of Things Wireless Sensor Network Sparse signal 

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References

  1. 1.
    Candes, E.: The Restricted Isometry Property and its Implications for Compressed Sensing. In: Compte Rendus de l’Academie des Sciences, Paris. Series I, vol. 346, pp. 589–592 (2008)Google Scholar
  2. 2.
    Candes, E., Tao, T.: Near Optimal Signal Recovery from Random Projections:Universal Encoding Strategies? IEEE Trans. on Information Theory 52(12), 5406–5425 (2006)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Candés, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: Exact signal re-construction from highly incomplete frequency information. Submitted to IEEE Trans. Inform. Theory (June 2004); Available on theArXiV preprint server:math.GM/0409186Google Scholar
  4. 4.
    Candés, E.J., Tao, T.: Decoding by linear programming. Submitted to IEEE Trans. Inform. Theory (December 2004)Google Scholar
  5. 5.
    Candés, E.J., Tao, T.: Near-optimal signal recovery from random projections and universal encoding strategies. Submitted to IEEE Trans. Inform. Theory (November 2004); Available on the ArXiV preprint server: math.CA/0410542Google Scholar
  6. 6.
    Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20, 33–61 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    DeVore, R.A., Jawerth, B., Lucier, B.J.: Image compression through wavelet transform coding. IEEE Trans. Inform. Theory 38(2), 719–746 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Wei, D., Milenkovic, O.: Subspace Pursuit for Compressive Sensing Signal Reconstruction. IEEE Transactions on Information Theory 55(5), 2230–2249 (2009)CrossRefGoogle Scholar
  9. 9.
    Needell, D., Vershynin, R.: Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit. Foundations of Computational Mathematics (9), 317–334 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Strohmer, T., Hermann, M.: Compressed Sensing Radar. In: IEEE Proc. Int. Conf. Acoustic, Speech, and Signal Processing 2008, pp. 1509–1512 (2008)Google Scholar
  11. 11.
    Tadmor, E.: Numerical methods for nonlinear partial differential equations. In: Encyclopedia of Complexity and Systems Science. Springer, Heidelberg (2009)Google Scholar
  12. 12.
    Talagrand, M.: Selecting a proportion of characters. Israel J. Math. 108, 173–191 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Tauböck, G., Hlawatsch, F., Rauhut, H.: Compressive Estimation of Doubly Selective Channels: Exploiting Channel Sparsity to Improve Spectral Efficiency in Multicarrier Transmissions (2009)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Computer Science of University of Science and Technology of ChinaHefeiChina

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