Design and Implementation of Low-Power Analog-to-Information Conversion for Environmental Information Perception
Design and Implementation of Low-Power Analog-to-Information Conversion for Environmental Information Perception
复制标题
用于环境信息感知的低功耗模拟信息转换的设计与实现
DOI：
10.3390/en10060753
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发表时间：
2017
期刊：
影响因子：
3.2
通讯作者：
Wang Ruchuan
中科院分区：
文献类型：
--
作者：
Shen Shu;Shan Yue;Sun Lijuan;Sun Jian;Zou Zhiqiang;Wang Ruchuan
由于传感节点通常具有有限的功率资源，因此以高效率和低功耗获取信号非常重要，特别是在大规模无线传感器网络（WSNs）应用中。压缩感知（CS）是一种新兴的信号采集和压缩方法，是传统信号处理方法的重要替代方案，是wsn的可行解决方案。在我们之前的工作中，我们研究了几种使用CS进行压缩采样和信号恢复的数据恢复算法和网络模型。结果在实际环境监测无线传感器网络的大型数据集上得到验证。在本文中，我们重点研究了信号采集和处理的硬件解决方案。我们提出了一种基于CS理论的模拟-信息转换器（AIC）范例。该系统模型由调制模块、滤波模块和采样模块组成，并在MATLAB/Simulink 7.0环境下进行了仿真和分析。此外，还介绍了改进后的数字AIC系统的硬件设计与实现。研究了三种不同贪心数据恢复算法的性能，并分析了系统功耗。实验结果表明，对于正常的环境信号，新系统克服了Nyquist极限，采样频率低，恢复性能好，适用于基于WSNs的环境监测。
Because sensing nodes typically have limited power resources, it is extremely important for signals to be acquired with high efficiency and low power consumption, especially in large-scale wireless sensor networks (WSNs) applications. An emerging signal acquisition and compression method called compressed sensing (CS) is a notable alternative to traditional signal processing methods and is a feasible solution for WSNs. In our previous work, we studied several data recovery algorithms and network models that use CS for compressive sampling and signal recovery. The results were validated on large data sets from actual environmental monitoring WSNs. In this paper, we focus on the hardware solution for signal acquisition and processing on separate end nodes. We propose the paradigm of an analog-to-information converter (AIC) based on CS theory. The system model consists of a modulation module, filtering module, and sampling module, and was simulated and analyzed in a MATLAB/Simulink 7.0 environment. Further, the hardware design and implementation of an improved digital AIC system is presented. We also study the performances of three different greedy data recovery algorithms and analyze the system power consumption. The experimental results show that, for normal environmental signals, the new system overcomes the Nyquist limit and exhibits good recovery performance with a low sampling frequency, which is suitable for environmental monitoring based on WSNs.