Development and experimental validation of self-powered wireless vibration sensor node using vibration energy harvester

Rubes, Ondrej; Chalupa, Jan; Ksica, Filip; Hadaš, Zdeněk

doi:10.1016/j.ymssp.2021.107890

Cited by 45 publications

(21 citation statements)

References 42 publications

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“…For this reason, operational tests of the kinetic energy harvester utilized as a source of energy for a wireless sensor node were conducted for both types of trains. Our lab successfully tested autonomous wireless sensor nodes with the vibration energy harvester in lab conditions, and these results, including the design of a power management, sensing unit and communication module, were successfully published in our research paper [ 28 ].…”

Section: Kinetic Energy Harvester As Source Of Energy For Wireless Se...mentioning

confidence: 99%

Kinetic Electromagnetic Energy Harvester for Railway Applications—Development and Test with Wireless Sensor

Hadaš

Rubes

Ksica

et al. 2022

Sensors

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This paper deals with a development and lab testing of energy harvesting technology for autonomous sensing in railway applications. Moving trains are subjected to high levels of vibrations and rail deformations that could be converted via energy harvesting into useful electricity. Modern maintenance solutions of a rail trackside typically consist of a large number of integrated sensing systems, which greatly benefit from autonomous source of energy. Although the amount of energy provided by conventional energy harvesting devices is usually only around several milliwatts, it is sufficient as a source of electrical power for low power sensing devices. The main aim of this paper is to design and test a kinetic electromagnetic energy harvesting system that could use energy from a passing train to deliver sufficient electrical power for sensing nodes. Measured mechanical vibrations of regional and express trains were used in laboratory testing of the developed energy harvesting device with an integrated resistive load and wireless transmission system, and based on these tests the proposed technology shows a high potential for railway applications.

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Section: Kinetic Energy Harvester As Source Of Energy For Wireless Se...mentioning

confidence: 99%

Kinetic Electromagnetic Energy Harvester for Railway Applications—Development and Test with Wireless Sensor

Hadaš

Rubes

Ksica

et al. 2022

Sensors

Self Cite

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“…It is best to look for a combination of all three; however, only EH can extend the maintenance-free time of the platform in a non-linear way and even beyond the expected lifetime. Many research centers are working on self-powered sensing circuits for integrating wireless sensing, energy harvesting, power management and energy storage, and data processing [10,11].Research also includes the development of antennas [12], energy harvesting from unusual sources such as hydraulic vibrations [13] or even nanotribological sources [14]. Interestingly, according to [15], the use of multi-scale sensors and biosensors in the multisensor platform could significantly improve the quality of detection in medicine as well, more effectively identifying possible SARS-CoV-19 virus infections.…”

Section: Study Areamentioning

confidence: 99%

“…The minimum lifetime for 1 battery charging is 24 h. Assuming that solar panel is able to fully charge the battery, the device is estimated to work as long as required without intervention. The whole day of the device's operating consumes the amount of charge calculated from Equation (10).…”

Section: Current Consumption Of the Zigbee End Devicementioning

confidence: 99%

Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor

et al. 2022

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Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people’s quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%).

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“…Several sources can be exploited to perform energy harvesting for wireless sensor nodes. Wind [ 12 ], electromagnetism [ 13 ], ambient radio frequency [ 14 ], microbial fuel cells [ 15 ], thermoelectricity [ 16 ] and vibrations [ 17 ] are some examples; nevertheless, the primacy belongs to the solar energy source. In particular, most of the applications exploit photovoltaic (PV) cells exposed to direct sunlight to provision energy for self-sufficient wireless sensor nodes [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Self-Sufficient Sensor Node Embedding 2D Visible Light Positioning through a Solar Cell Module

Cappelli

Carli

Fort

et al. 2022

Sensors

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Nowadays, indoor positioning (IP) is a relevant aspect in several scenarios within the Internet of Things (IoT) framework, e.g., Industry 4.0, Smart City and Smart Factory, in order to track, amongst others, the position of vehicles, people or goods. This paper presents the realization and testing of a low power sensor node equipped with long range wide area network (LoRaWAN) connectivity and providing 2D Visible Light Positioning (VLP) features. Three modulated LED (light emitting diodes) sources, the same as the ones commonly employed in indoor environments, are used. The localization feature is attained from the received light intensities performing optical channel estimation and lateration directly on the target to be localized, equipped with a low-power microcontroller. Moreover, the node exploits a solar cell, both as optical receiver and energy harvester, provisioning energy from the artificial lights used for positioning, thus realizing an innovative solution for self-sufficient indoor localization. The tests performed in a ~1 m2 area reveal accurate positioning results with error lower than 5 cm and energy self-sufficiency even in case of radio transmissions every 10 min, which are compliant with quasi-real time monitoring tasks.

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Development and experimental validation of self-powered wireless vibration sensor node using vibration energy harvester

Cited by 45 publications

References 42 publications

Kinetic Electromagnetic Energy Harvester for Railway Applications—Development and Test with Wireless Sensor

Kinetic Electromagnetic Energy Harvester for Railway Applications—Development and Test with Wireless Sensor

Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor

Self-Sufficient Sensor Node Embedding 2D Visible Light Positioning through a Solar Cell Module

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