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Scalable Lightweight IoT-Based Smart Weather Measurement System
The Internet of Things (IoT) plays a critical role in remotely monitoring a wide variety of different application sectors, including agriculture, building, and energy. The wind turbine energy generator (WTEG) is a real-world application that can take advantage of IoT technologies, such as a low-cost...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301168/ https://www.ncbi.nlm.nih.gov/pubmed/37420735 http://dx.doi.org/10.3390/s23125569 |
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author | Albuali, Abdullah Srinivasagan, Ramasamy Aljughaiman, Ahmed Alderazi, Fatima |
author_facet | Albuali, Abdullah Srinivasagan, Ramasamy Aljughaiman, Ahmed Alderazi, Fatima |
author_sort | Albuali, Abdullah |
collection | PubMed |
description | The Internet of Things (IoT) plays a critical role in remotely monitoring a wide variety of different application sectors, including agriculture, building, and energy. The wind turbine energy generator (WTEG) is a real-world application that can take advantage of IoT technologies, such as a low-cost weather station, where human activities can be significantly affected by enhancing the production of clean energy based on the known direction of the wind. Meanwhile, common weather stations are neither affordable nor customizable for specific applications. Moreover, due to weather forecast changes over time and location within the same city, it is not efficient to rely on a limited number of weather stations that may be located far away from a recipient’s location. Therefore, in this paper, we focus on presenting a low-cost weather station that relies on an artificial intelligence (AI) algorithm that can be distributed across a WTEG area with minimal cost. The proposed study measures multiple weather parameters, such as wind direction, wind velocity (WV), temperature, pressure, mean sea level, and relative humidity to provide current measurements to recipients and AI-based forecasts. In addition, the proposed study consists of several heterogeneous nodes and a controller for each station in a target area. The collected data can be transmitted through Bluetooth low energy (BLE). The experimental results reveal that the proposed study matches the standard of the National Meteorological Center (NMC), with a nowcast measurement of 95% accuracy for WV and 92% for wind direction (WD). |
format | Online Article Text |
id | pubmed-10301168 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103011682023-06-29 Scalable Lightweight IoT-Based Smart Weather Measurement System Albuali, Abdullah Srinivasagan, Ramasamy Aljughaiman, Ahmed Alderazi, Fatima Sensors (Basel) Article The Internet of Things (IoT) plays a critical role in remotely monitoring a wide variety of different application sectors, including agriculture, building, and energy. The wind turbine energy generator (WTEG) is a real-world application that can take advantage of IoT technologies, such as a low-cost weather station, where human activities can be significantly affected by enhancing the production of clean energy based on the known direction of the wind. Meanwhile, common weather stations are neither affordable nor customizable for specific applications. Moreover, due to weather forecast changes over time and location within the same city, it is not efficient to rely on a limited number of weather stations that may be located far away from a recipient’s location. Therefore, in this paper, we focus on presenting a low-cost weather station that relies on an artificial intelligence (AI) algorithm that can be distributed across a WTEG area with minimal cost. The proposed study measures multiple weather parameters, such as wind direction, wind velocity (WV), temperature, pressure, mean sea level, and relative humidity to provide current measurements to recipients and AI-based forecasts. In addition, the proposed study consists of several heterogeneous nodes and a controller for each station in a target area. The collected data can be transmitted through Bluetooth low energy (BLE). The experimental results reveal that the proposed study matches the standard of the National Meteorological Center (NMC), with a nowcast measurement of 95% accuracy for WV and 92% for wind direction (WD). MDPI 2023-06-14 /pmc/articles/PMC10301168/ /pubmed/37420735 http://dx.doi.org/10.3390/s23125569 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Albuali, Abdullah Srinivasagan, Ramasamy Aljughaiman, Ahmed Alderazi, Fatima Scalable Lightweight IoT-Based Smart Weather Measurement System |
title | Scalable Lightweight IoT-Based Smart Weather Measurement System |
title_full | Scalable Lightweight IoT-Based Smart Weather Measurement System |
title_fullStr | Scalable Lightweight IoT-Based Smart Weather Measurement System |
title_full_unstemmed | Scalable Lightweight IoT-Based Smart Weather Measurement System |
title_short | Scalable Lightweight IoT-Based Smart Weather Measurement System |
title_sort | scalable lightweight iot-based smart weather measurement system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301168/ https://www.ncbi.nlm.nih.gov/pubmed/37420735 http://dx.doi.org/10.3390/s23125569 |
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