Distributed compressive sensing in heterogeneous sensor network

In this paper, we apply distributed compressive sensing (DCS) in heterogeneous sensor network (HSN). Combining different types of measurement matrices and different numbers of measurements, we firstly investigate three different scenarios in which HSN is used for signal acquisition. In the first scenario, there are two different types of measurement matrices. One is Gaussian measurement and the other is Fourier measurement, and each sensor applies the same numbers of measurements. In the second scenario, all sensors use the same type of measurement matrices but the number of measurements are different with each other. The third scenario combines different types of measurement matrix and distinct numbers of measurements. Our simulation results show that in Scenario I, when the common sparsity is considerable, the DCS scheme can reduce the number of measurements. In Scenario II, the reconstruction situation becomes better with the increase of the number of measurements. In both Scenarios I and III, joint decoding that use different types of measurement matrices performs better than that of all-Gaussian measurement matrices, but it performs worse than that of all-Fourier measurement matrices. Therefore, DCS is a good compromise between reconstruction percentage and the number of measurements in HSN.