Before revision:  The key idea is to compress the  signal samples on the mote platform by efficient random projections and transfer them to a central device , where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain. Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework , together with the proposed correlation domain achieved up to two orders of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling.
After revision:  The key idea is to compress the signal samples on the mote platform by efficient random projections and transfer them to a central device , where a convex optimization process estimates the range by exploiting the sparse signal structure in the proposed correlation dictionary. Through theoretical validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework , together with the proposed correlation domain achieved up to two orders of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling.