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
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

Sensors (Basel). 2013 Mar 13;13(3):3473-500
pubmed: 23486217
BMC Infect Dis. 2018 Apr 17;18(1):183
pubmed: 29665781
Sensors (Basel). 2020 Jun 03;20(11):
pubmed: 32503318
Sensors (Basel). 2021 Jun 01;21(11):
pubmed: 34205904

Auteurs

Abdullah Albuali (A)

Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Ramasamy Srinivasagan (R)

Department of Computer Engineering, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Ahmed Aljughaiman (A)

Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Fatima Alderazi (F)

Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

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Classifications MeSH