Sensing , Compression and Recovery for Wireless Sensor Networks : Sparse Signal Modelling

  title={Sensing , Compression and Recovery for Wireless Sensor Networks : Sparse Signal Modelling},
  author={Riccardo Masiero and Giorgio Quer and Gianluigi Pillonetto and Michele Rossi and Michele Zorzi},
In this paper, we propose a sparsity model that allows the use of Compressive Sensing (CS) for the online recovery of large data sets in real Wireless Sensor Network (WSN) scenarios. We advocate the joint use of CS for the recovery and of Principal Component Analysis (PCA) to capture the spatial and temporal characteristics of real signals. The statistical characteristics of the signals are thus exploited to design the sparsification matrix required by CS recovery. In this paper, we represent… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 24 references

Bayesian Interpolation

  • D. J. MacKay
  • Neural Computation Journal, vol. 4, no. 3, pp…
  • 1992
Highly Influential
4 Excerpts

SCoRe1: Sensing Compression and Recovery through Online Estimation for Wireless Sensor Networks

  • G. Quer, R. Masiero, M. Rossi, M. Zorzi
  • Submitted to IEEE Trans. on Wireless…
  • 2011
1 Excerpt

Sense&Sensitivity: A Large-Scale Experimental Study of Reactive Gradient Routing

  • T. Watteyne, D. Barthel, M. Dohler, I. Auge-Blum
  • Measurement Science and Technology, Special Issue…
  • 2010
2 Excerpts

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