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Energies 2017, 10(6), 753; doi:10.3390/en10060753

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

1
School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2
Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Academic Editor: Silvio Simani
Received: 22 March 2017 / Revised: 17 May 2017 / Accepted: 22 May 2017 / Published: 27 May 2017

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. View Full-Text
Keywords: compressed sensing; analog-to-information converter; data recovery; environmental information perception; wireless sensor network compressed sensing; analog-to-information converter; data recovery; environmental information perception; wireless sensor network
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MDPI and ACS Style

Shen, S.; Shan, Y.; Sun, L.; Sun, J.; Zou, Z.; Wang, R. Design and Implementation of Low-Power Analog-to-Information Conversion for Environmental Information Perception. Energies 2017, 10, 753.

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