基于传感云技术和改进压缩感知的物联网数据处理方法

Internet of things data processing based on sensor cloud and improved compressive sensing

压缩感知通过稀疏采样技术,可以在远程客户端通过少量数据信息实现数据的重构,这给压缩感知和互联网结合提供了理论基础。针对传统压缩感知算法处理效率低和质量差的缺点,将曲波变换和图像分块理论引入压缩感知,提出一种基于传感云和改进压缩感知的物联网路数据处理架构。结果表明,通过无噪图像、加噪图像和不同采样频率对重构图像质量影响的研究发现,与SIDCT和PBDCT相比较,本文方法PB⁃DCT具有更强的抵抗噪声的能力,极大地提高传感网络的数据处理和数据存储能力。

Through sparse sampling,compressive sensing can reconstruct data through a small amount of data information on a remote client,which provides a theoretical basis for the combination of compressed sensing and the Internet.Aiming at the disadvantages of low efficiency and poor quality of traditional compressive sensing algorithm,the curved wave transform and image block theory were introduced into compressive sensing,and a data processing architecture for Internet of Things(IoT)based on sensor cloud and improved compressed sensing was proposed.The results showed that compared with SIDCT and PBDCT,the proposed method PBDCT had a stronger ability to resist noise,which greatly improves the data processing and data storage capabilities of the sensor network.