基于模拟信息转换器的物联网海量数据处理研究

针对大规模物联网应用的海量数据处理信息获取效率低、硬件成本昂贵的问题, 依据压缩感知(compressed sensing, CS)理论, 建立了一种模拟信息转换器(analog to information converter, AIC)数据处理系统模型。模型以MATLAB/Simulink为平台, 分别设计了信号的解调、过滤、采样、重构等功能模块, 并对不同频率分量的信号进行处理。实验结果表明, 该模型可以较低采样率、高压缩比精确重构稀疏信号, 重构效率与观测数M、抽取行数K以及信号频率分量相关。

Research on IOT mass data processing based on analog to information converter

For inefficiency information acquisition and costly hardware of mass data processing in large-scale applications of Internet of Things, this paper built an analog to information converter(AIC) data processing system model based to compressed sensing(CS). It designed signal demodulation module, filtering module, sampling module, and reconstruction module to processing signals which contained different frequency components through MATLAB/Simulink. Experiments show that, the mo-del achieves low speed sampling and accurate reconstruction of sparse signal, and the sparse signal reconstruction efficiency and quality are related to observations value M, rows extraction value K and signal frequency components.