Comparative Analysis on Transform and Reconstruction of Compressed Sensing in Sensor Networks

Compressed sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It holds valuable implications for wireless sensor networks because power and bandwidth are limited resources. In this paper, applying the theory of compressed sensing to the practical sensor network data recovery problem, we compare the performance of different CS reconstruction algorithms combined with wavelet and discrete cosine transform (DCT) basis. We demonstrate empirically that DCT is good for sinusoid oscillatory data while wavelet is good for data with point-like singularities. Furthermore, comparison on reconstruction algorithms shows basis pursuit (BP) is best in term of PSNR performance and computing time. In addition, benefit of CS for noisy channel of sensor network is tested and how to achieve good performance in noisy channel is discussed.