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Compressed sensing(CS) has been applied widely in Wireless sensor networks(WSNs) recently.An optimal Compressed data gathering(CDG) framework for energy efficient WSNs is proposed here. A novel Measurement matrix optimization algorithm(MMOA) is proposed for compressed data measurement in WSNs. Diffusion wavelet transform matrix(DWTM) is chosen for sparse representation of the compressed data. An Optimal data aggregation tree(ODAT) algorithm is presented based on CS and routing technology. MMOA is to reduce the data transmissions under the same data reconstruction ratio. DWTM is to make the original data become more sparse and to increase the compressed data reconstruction ratio. The main purpose of ODAT is to minimize the energy consumption of the whole WSNs through the CDG technology and the optimal route. We validate the efficiency of the proposed CDG framework based on MMOA, DWTM and ODAT through extensive experiments.
A novel Measurement Matrix Optimization Algorithm (MMOA) has been proposed for compressed data measurement (CS) in the WSNs. Diffusion wavelet transform matrix (DWTM) is chosen for sparse representation of the compressed data. An Optimal data aggregation tree (ODAT) algorithm is presented based on CS and routing technology. MMOA is to reduce the data transmissions under the same data reconstruction ratio. DWTM is to make the original data become more sparse and to increase the compressed data reconstruction ratio. The main purpose of ODAT is to minimize the energy consumption of the whole WSNs through the CDG technology and the optimal route. We validate the efficiency of the proposed CDG framework based on MMOA, DWTM and ODAT through extensive experiments.