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.
Highlights
Due to the rapid economic development of China, environmental pollution affected by transportation and industry is becoming one of the biggest problems in China’s society today.With the help of those information and communication technologies (ICTs), how to monitor and control the environment pollution effectively has gained much attention from academia and industry, and is becoming a hot research issue
It is able to obtain its corresponding signal X (k) in frequency domain through Discrete-Time Fourier Transform (DFT), as shown in Figure 7, where Fs stands for sampling frequency
This paper presented a compressed sensing (CS)-based signal acquisition system that solves the limited energy problem of sensor nodes in environmental monitoring wireless sensor networks (WSNs)
Summary
Due to the rapid economic development of China, environmental pollution affected by transportation and industry is becoming one of the biggest problems in China’s society today. With the help of those information and communication technologies (ICTs), how to monitor and control the environment pollution effectively has gained much attention from academia and industry, and is becoming a hot research issue As one of such ICTs, wireless sensor networks (WSNs) [1]. In our previous work [23,24,25,26], we focused on software solution on network, which aims at designing suitable compressive algorithms or network models to solve the problem of data acquisition during transmission in large distributed WSNs. In our previous work [23,24,25,26], we focused on software solution on network, which aims at designing suitable compressive algorithms or network models to solve the problem of data acquisition during transmission in large distributed WSNs Another alternative way to decrease the energy consumption and transmission costs is designing low-power AIC device on separate end node for compressive signal acquisition by reducing the sampling rate of signal, which is our target in the work of this paper, and we name it hardware solution on end node.
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