基于字典学习和压缩感知的WSN数据压缩
Computer Engineering and Design（2022）
太原理工大学
Abstract
针对无线传感网中,监测数据特征差异、节点能耗受限、现有数据压缩方法效率较低等问题,提出一种基于字典学习和压缩感知的数据压缩模型.改进K-SVD字典学习算法训练稀疏基,满足监测数据特征差异较大的特点,将稀疏变换由感知节点转移到基站,减轻感知节点能耗,降低压缩复杂度.在相同数据集上与现有的OEGMP算法以及基于DCT稀疏基的压缩感知算法做对比实验,其结果表明,在压缩率为0.2时,恢复数据均方根误差由0.281和0.260降低至0.093,模型在数据压缩率和恢复精度等方面有明显提升,可满足大部分实际应用需求.
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