Energy-Efficient Spectral Analysis Method Using Autoregressive Model-Based Approach for Internet of Things

• Published in: IEEE transactions on circuits and systems. I, Regular papers Vol. 66; no. 10; pp. 3896 - 3905
• Main Authors: Yoshida, Seiya, Izumi, Shintaro, Kajihara, Koichi, Yano, Yuji, Kawaguchi, Hiroshi, Yoshimoto, Masahiko
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Autocorrelation; Autoregressive (AR) model; Autoregressive models; Autoregressive processes; Bluetooth; Coefficients; Correlation; data compression; Data processing; Discrete Fourier transforms; Distributed processing; edge computing; Energy consumption; energy efficient devices; Fast Fourier transformations; Fourier transforms; Internet of Things; low power devices and circuits; Mathematical model; Spectra; Spectral analysis; Spectrum analysis; Yule–Walker method
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2019.2922990

This paper presents an energy-efficient spectral analysis method for the Internet of Things (IoT). The objective of this paper is to reduce the energy consumption of edge devices. The proposed method uses an autoregressive (AR) model for spectral analysis instead of the discrete Fourier transform, and its calculation process is distributed to the edge device and a base station by considering the energy consumption tradeoff of the data processing and the data communication. In this paper, the Yule-Walker method is employed for the AR coefficient calculation. The calculation process of Yule-Walker method can be divided into two parts: an autocorrelation calculation and an AR coefficient calculation. The autocorrelation calculation is implemented in the edge devices, and its dedicated hardware is designed using Verilog HDL. Meanwhile, the AR coefficient is calculated in the base station and is used for the spectral analysis. According to this distributed processing approach, the energy consumption of the edge device can be reduced compared with conventional DFT approaches using the fast Fourier transform (FFT). The system level energy consumption is evaluated assuming the IoT edge device, which has a wireless transceiver using Bluetooth low energy. The evaluation results show that the proposed method can reduce 79% of the edge device energy consumption for spectral analysis in a practical application.