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Compressive Sensing (CS) has been applied successfully in a wide variety of applications in recent years, including photography, holography, optical system research, facial recognition, and Medical Resonance Imaging (MRI). In wireless sensor networks (WSNs), significant research work has been pursued to investigate the use of CS to reduce the amount of data communicated, particularly in data aggregation applications and thereby improving energy efficiency. However, most of the previous work in WSN has used CS under the assumption that data field is smooth with negligible white Gaussian noise. In these schemes signal sparsity is estimated globally based on the entire data field, which is then used to determine the CS parameters. In more realistic scenarios, where data field may have regional fluctuations or it is piecewise smooth, existing CS based data aggregation schemes yield poor compression efficiency. In order to take full advantage of CS in WSNs, we propose an adaptive aggregation scheme referred to as Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS). The proposed schemes dynamically determines sparsity values based on signal variations in local regions. We prove that A-HDACS enables more sensor nodes to employ CS compared to the schemes that do not adapt to the changing field. Also, the simulation results demonstrate improvement in energy efficiency and accuracy in signal recovery.

Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS) for Non-Smooth Data Field