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Compressed Data Acquisition and Progressive Signal Recovery in Wireless Sensor Networks

We consider compressed acquisition and progressive reconstruction of spatially and temporally correlated data in wireless sensor networks (WSNs). We propose a novel, sliding window based compressed sensing (CS) method in which the sink can instantaneously reconstruct WSN samples from periodically acquired CS measurements. Moreover, the prior information attained by decoding the WSN readings multiple times allows the method to progressively refine the signal estimates. Numerical results exemplify that our proposed method results in higher CS recovery accuracy, yet with lower decoding delay and complexity, as compared to the state of the art methods.