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

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Executive Summary

In this paper, the authors 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. They 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, they represent this framework through a Bayesian Network (BN) and they use Bayesian analysis to infer and approximate the statistical distribution of the principal components. They show that the Laplacian distribution provides an accurate representation of the statistics of the data measured from real WSN testbeds.

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