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Sub-Nyquist sampling techniques for Wireless Sensor Networks (WSN) are gaining increasing attention as an alternative method to capture natural events with desired quality while minimizing the number of active sensor nodes. Among those techniques, Compressive Sensing (CS) approaches are of special interest, because of their mathematically concrete foundations and efficient implementations. The authors describe how the geometrical representation of the sampling problem can influence the effectiveness and efficiency of CS algorithms. In this paper they introduce a Map-based model which exploits redundancy attributes of signals recorded from natural events to achieve an optimal representation of the signal.
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