A Novel Self-Powered Wireless Sensor Node Based on Energy Harvesting for Mechanical Vibration Monitoring

Zhang, Xihai; Fang, Junlong; Meng, Fanfeng; Wei, Xiaoli

doi:10.1155/2014/642365

Cited by 13 publications

(8 citation statements)

References 14 publications

(14 reference statements)

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“…Common environmental energy sources that wireless sensor nodes could harvest and convert to electric power include solar energy [ 16 , 17 , 18 ], wind energy [ 19 , 20 , 21 ], radio-frequency (RF) energy [ 22 , 23 , 24 ], and vibration mechanical energy [ 25 , 26 , 27 ]. The photovoltaic cell, as an energy-harvesting device for solar energy with high efficiency and mature commercialization, has been widely used in the field of self-powered sensor system.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Low Frequency Eccentric Pendulum-Based Electromagnetic Vibrational Energy Harvester

Deng

Zhang

et al. 2020

Micromachines

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With the development of low-power technology in electronic devices, the wireless sensor network shows great potential in applications in health tracing and ocean monitoring. These scenarios usually contain abundant low-frequency vibration energy, which can be collected through appropriate energy conversion architecture; thus, the common issue of limited battery life in wireless sensor devices could be solved. Traditional energy-converting structures such as the cantilever-beam type or spring-mass type have the problem of high working frequency. In this work, an eccentric pendulum-based electromagnetic vibration energy harvester is designed, analyzed, and verified with the finite element analysis method. The pendulum that contains alternative distributed magnets in the outer side works as a rotor and has the advantages of a simple structure and low center frequency. The structure size is well scalable, and the optimal output performance can be obtained by optimizing the coil thickness and width for a given diameter of the energy harvester. The simulation results show that the energy harvester could work in ultra-low frequencies of 0.2–3.0 Hz. A full-scale prototype of the energy harvester is manufactured and tested. The center working frequency is 2.0 Hz with an average output power of 8.37 mW, which has potential for application in driving low-power wireless sensor nodes.

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Section: Introductionmentioning

confidence: 99%

Ultra-Low Frequency Eccentric Pendulum-Based Electromagnetic Vibrational Energy Harvester

Deng

Zhang

et al. 2020

Micromachines

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“…More specifically, many current research challenges stem from the massive deployment of the Internet-of-Things (IoT) sensors (e.g. environmental sensors [1], industrial sensors [4], wearables [2], [3], etc.). This brings the need for development of effective power supply solutions.…”

Section: Introductionmentioning

confidence: 99%

Powering Batteryless Embedded Platforms by Piezoelectric Transducers: A Pilot Study

Prauzek¹,

Konecny²,

Vitasek³

et al. 2019

ElAEE

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This contribution presents a pilot study on powering battery-less embedded systems. First, a piezoelectric transducer principle and low-power techniques are reviewed in the background section. In the experimental part, the authors describe a testing setup consisting of piezoelectric transducer, DC/DC converter with energy storage, and evaluation microcontroller platform (FRDM KL25Z). Three types of experiments have been conducted for two voltage configurations including charging speed, continuous operation and discharge test. Results presented in this article concentrate on power supply voltage (1.8 V and 3.3 V), total efficiency (67.16 % and 76.75 %) and operation times (24.28 s and 15 s) of the embedded system.

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“…Therefore, it is very necessary for the IoT to estimate missing values. Missing value imputation exists in various domains, such as computer and network domain [5], economy domain [6], medical domain [7], and psychological domain [8]. This paper addresses missing value imputation for the Internet of Things.…”

Section: Introductionmentioning

confidence: 99%

Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things

Yan

Xiong

et al. 2015

Mathematical Problems in Engineering

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This paper addresses missing value imputation for the Internet of Things (IoT). Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL), model of missing value imputation based on binary search (MBS), and model of missing value imputation based on Gaussian mixture model (MGI). Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively.

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A Novel Self-Powered Wireless Sensor Node Based on Energy Harvesting for Mechanical Vibration Monitoring

Cited by 13 publications

References 14 publications

Ultra-Low Frequency Eccentric Pendulum-Based Electromagnetic Vibrational Energy Harvester

Ultra-Low Frequency Eccentric Pendulum-Based Electromagnetic Vibrational Energy Harvester

Powering Batteryless Embedded Platforms by Piezoelectric Transducers: A Pilot Study

Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things

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