Compressed acquisition and progressive reconstruction of multi-dimensional correlated data in wireless sensor networks

This paper addresses compressed acquisition and progressive reconstruction of spatially and temporally correlated signals in wireless sensor networks (WSNs) via compressed sensing (CS). We propose a novel method based on sliding window processing, where the sink periodically collects CS measurements of sensor samples, and then, instantaneously reconstructs current WSN samples by exploiting the spatio-temporal correlation via Kronecker sparsifying bases. By using previous estimates as prior information, the method can progressively improve the reconstruction accuracy of the signal ensemble. Furthermore, the method can control the trade-off between decoding delay and complexity. Numerical results demonstrate that the proposed method can recover WSN data samples from CS measurements with higher reconstruction accuracy, yet with lower decoding delay and complexity, as compared to the state of the art methods.