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Localization of wireless sensors using compressive sensing for manifold learning
2009
2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications
In this paper, a novel compressive sensing for manifold learning protocol (CSML) is proposed for localization in wireless sensor networks (WSNs). Intersensor 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. We represent the pair-wise distance measurement as a sparse matrix. Instead of sending full pair-wise measurement data to
doi:10.1109/pimrc.2009.5449918
dblp:conf/pimrc/FengVT09
fatcat:s5ytvrnkqvc4djg62vb3ckn6t4