Reordering for Better Compressibility: Efficient Spatial Sampling in Wireless Sensor Networks

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Executive Summary

Compressed Sensing (CS) is a novel sampling paradigm that tries to take data-compression concepts down to the sampling layer of a sensory system. It states that discrete compressible signals are recoverable from sub-sampled data, when the data vector is acquired by a special linear transform of the original discrete signal vector. Distributed sampling problems especially in Wireless Sensor Networks (WSN) are good candidates to apply CS and increase sensing efficiency without sacrificing accuracy. In this paper, the authors discuss how to reorder the samples of a discrete spatial signal vector by defining an alternative permutation of the Sensor Nodes (SN). Accordingly, they propose a method to enhance CS in WSN through improving signal compressibility by finding a sub-optimal permutation of the SNs.

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