Design and Implementation of Low-Power Analog-to-Information Conversion for Environmental Information Perception

Shu Shen, Yue Shan, Lijuan Sun, Jian Sun, Zhiqiang Zou and Ruchuan Wang
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Shu Shen: School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Yue Shan: School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Lijuan Sun: School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Jian Sun: School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Zhiqiang Zou: School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Ruchuan Wang: School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Energies, 2017, vol. 10, issue 6, 1-20

Abstract: 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.

Keywords: compressed sensing; analog-to-information converter; data recovery; environmental information perception; wireless sensor network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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