Scalable Lightweight IoT-Based Smart Weather Measurement System.
conventional neural network
decision tree
low-cost weather station
meteorology
tiny machine learning (TinyML)
wind speed
wind velocity
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
14 Jun 2023
14 Jun 2023
Historique:
received:
16
04
2023
revised:
24
05
2023
accepted:
08
06
2023
medline:
10
7
2023
pubmed:
8
7
2023
entrez:
8
7
2023
Statut:
epublish
Résumé
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).
Identifiants
pubmed: 37420735
pii: s23125569
doi: 10.3390/s23125569
pmc: PMC10301168
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia
ID : INST039
Références
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