Compressive Sensing Optimization for Signal Ensembles in WSNs

Data Compression And Visualization For Wireless Sensor Networks

“…In [4], the authors discuss the issue of exploiting the inter-signal correlations present in WSN data sets to achieve a better compression factor or a better reconstruction quality under energy for medium-sized networks, confirming the suitability of DCS for signal recovery in WSNs.…”

A Distributed Compressed Sensing-based Algorithm for the Joint Recovery of Signal Ensemble

“…In [9], influences of compressive sensing parameters in compression of a common set of artificial signal on nodes' lifetime is partially discussed. Also in [10], by adjusting sampling rate, a sparse generated matrix is proposed to maintain an acceptable signal reconstruction performance.…”

Design of a compressive data gathering scheme with an energy efficient reconstruction algorithm for wsn

“…Qing Ling.et.al.in [14] have developed an optimal decentralized algorithm for sparse signal recovery and demonstrates its application in monitoring localized phenomena using energy-constrained large-scale wireless sensor networks. Liu Xiang.et.al.in [15] have investigated the application of CS to data collection in wireless sensor networks, and aim at minimizing the network energy consumption through joint routing and compressed aggregation. Author first characterize the optimal solution to this optimization problem, and then prove its NPcompleteness.…”

“…Where, z = uncompressed signal, = compressed signal, n = noise h) Mathematical Model for Compressive Reconstruction Scheme i. Autocorrelation function on original signal Let's say autocorrelation function R applied to the Original random signal x (Nx1) and it will give the signal let's say y (Nx1) [15] R (8) ii. Sparse representation of the auto correlated signal Signal y (Nx1) will have a sparse expression on the represent basis £ (N x N), N is the data length of signal y [18]: …”