Compressive Sensing (CS) shows high promise for fully distributed compression in wireless sensor networks (WSNs). In theory, CS allows the approximation of the readings from a sensor field with excellent accuracy, while collecting only a small fraction of them at a data gathering point. However, the conditions under which CS performs well are not necessarily met in practice. CS requires a suitable transformation that makes the signal sparse in its domain.
Also, the transformation of the data given by the routing protocol and network topology and the sparse representation of the signal have to be incoherent, which is not straightforward to achieve in real networks. In this paper we investigated the effectiveness of data recovery through joint Compressive Sensing (CS) and Principal Component Analysis (PCA) in actual WSN deployments.
We proposed a novel system, called CS-PCA that embeds a feedback control mechanism to automatically change the compression ratio through changing the number of transmitting sensors, while bounding the reconstruction error. The considered recovery techniques in the proposed system are: biharmonic Spline (Spline), Deterministic Ordinary Least Square (DOLS), Probabilistic Ordinary Least Square (POLS) and Joint CS and PCA (CS-PCA). We found that the later outperform all other interpolation technique in the case of slow varying signals, while POLS was the most effective in case of fast varying signals that(low correlation less than 0.45)
Al-Baath University Journal.
2014.
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