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In this paper, a novel Compressive Sensing for Manifold Learning protocol (CSML) is proposed for localization in Wireless Sensor Networks (WSNs). Inter-sensor communication costs are reduced significantly by applying the theory of compressive sensing, which indicates that sparse signals can be recovered from far fewer samples than that needed by the Nyquist sampling theorem. The authors represent the pair-wise distance measurement as a sparse matrix. Instead of sending full pair-wise measurement data to a central node, each sensor transmits only a small number of compressive measurements.
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