Rate adaptive compressed sampling based on region division for wireless sensor networks.

Abstract

Image acquisition and transmission in wireless sensor networks (WSN) are core issues for some resource-deficient multimedia sensing applications. Reducing sampling rates and data transmission lowers sensor node costs and energy, addressing communication bottlenecks. Block compressed sampling (BCS) can meet the above requirements. For BCS, the sparsity or smoothness of the block signal is a crucial parameter, which determines the setting range of the sampling rate. For the sampling side of the sensor node, we cannot directly obtain the complete digital signal. Therefore, it becomes difficult to perform adaptive rate compressed sampling. In this paper, a novel adaptive sampling rate allocation scheme based on region division is proposed. First, we use a simple auxiliary vector to determine the complex and smooth regions of the current image. For the smooth region, we use a mean vector to divide each block into a residual block and a mean value block. Then the proposed prior probability sparsity estimation model is used to estimate the sparsity order of each residual block, while each mean value block requires only one measurement to restore losslessly. For the complex region, we first set a higher baseline sampling rate for it, and then adaptively allocate the remaining supplementary sampling rate based on the statistical characteristics of each block itself. Experiment results show that the proposed scheme can allocate an appropriate sampling rate to each block, reduce the total sampling rate, and significantly improve the signal reconstruction quality simultaneously.