IoT monitoring system for air quality assessment and collecting data

Tayeb, Mustapha Si; Benallal, Mohamed Anis; Benabadji, Mohammed Salim; Houari, A

doi:10.11591/ijeecs.v28.i3.pp1592-1600

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Table 5 establishes a comparison of some reported works based on the adopted methodologies and used technologies in these research works (Hasanh et al 2021 ; Dayananda et al 2021 ; Saini et al 2022 ; Si Tayeb et al 2022 ), with our proposed model. In contrast, although the authors (Dayananda et al 2021 ; Si Tayeb et al 2022 ) were successful in obtaining values for some dangerous gases such as CO 2 , CO, NH 3 , and NO 2 , but they did not use this information to assess air quality. Furthermore, authors (Hasanh et al 2021 ; Dayananda et al 2021 ; Saini et al 2022 ) provided no solution for visualizing the meteorological data collected remotely.…”

Section: Discussionmentioning

confidence: 99%

Modeling and implementation of a low-cost IoT-smart weather monitoring station and air quality assessment based on fuzzy inference model and MQTT protocol

Fahim

Mhouti

Boudaa

et al. 2023

Model. Earth Syst. Environ.

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The automatic weather system serves to inform farmers, tourists, planners, and others with precise information to help them take the appropriate action. Today, with the advancement of smart technologies, the system has evolved into many sensing methods to gather real-time climate data. This article investigates the modeling and implementation of a low-cost weather station device that also functions to measure air quality. The proposed system based on the Internet of Things (IoT) allows access to real-time climate data for a given area. This system monitors environmental conditions such as ambient temperature, humidity, atmospheric pressure, altitude, and levels of harmful atmospheric gases like CO 2 and NO 2 . This real-time telemetry device uses MQ-135, DHT-11 and BMP280 sensors to gather data. The ESP32 board processes the obtained data from all sensors. Additionally, we present a model for a fuzzy inference system (FIS) that performs parameter categorization using a reasoning procedure and incorporates the results into an air quality index (AQI) that describes the levels of pollution for Al Hoceima city. The FIS takes CO 2 and NO 2 values as input and returns the AQI. The AQI for Al Hoceima city is categorized into six levels: Excellent , Good , Regular , Bad , Dangerous , and Very Dangerous . Furthermore, the suggested system's block hardware employs the Message Queuing Telemetry Transport (MQTT) protocol to broadcast collected data to a mobile and web application via the Internet. The suggested IoT-embedded device was tested in real life, and the results were promising.

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Section: Discussionmentioning

confidence: 99%

Modeling and implementation of a low-cost IoT-smart weather monitoring station and air quality assessment based on fuzzy inference model and MQTT protocol

Fahim

Mhouti

Boudaa

et al. 2023

Model. Earth Syst. Environ.

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“…[29][30] These statistics were tested based on a set of measured long-term data when compared with related previous studies. [31][32][33]…”

Section: Data Quality Control and Analysis Methodsmentioning

confidence: 99%

Development of a hot weather warning tool for heat index monitoring in Thailand

paengkaew,

Limsakul,

Kokkaew

et al. 2023

J Public Hlth Dev

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This study aims to develop and test an extremely hot weather warning tool. The tool consists of a Node MCU ESP8266 microcontroller, air temperature and relative humidity sensor (DHT22), real time clock module (DS3231), micro-SD-card adapter (HW-125), display screen (2004A), and breadboard-shield. It is connected and controlled by Arduino IDE program. Its measurement ranges for temperature, humidity, and heat index are <10 to >50°C, 0 to 100%, and <27 to >50°C, respectively. This device also presents real-time health impacts by displaying one of five warning levels, namely 1) normal level (HI < 27°C), 2) caution level (HI 27°C to <32°C), 3) extreme caution level (HI 32°C to <41°C), 4) danger level (HI 41°C to <54°C), and 5) extreme danger level (HI ≥ 54°C). The data can be directly transferred to a computer by a card adapter. Based on validation with the occupational health and safety standards instrument, no outliers or missing data were found. HIEHT had a highly positive correlation with HIQT36 (r=0.99; p<0.01; n=4,182). Heat Index data measured by this tool was found to have acceptable values with a bias of 0.54 and RMSE of 0.99. For the pilot areas tested, it was found that the extremely hot weather warning tool can fully measure and record, as well as continuously display the results according to the tested date and time. These results show that the developed tool is a simple, easy-to-use, inexpensive, tiny, and portable instrument that can be used to monitor and measure heat to widely communicate extremely hot weather warning information to vulnerable groups and the general public in the community.

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“…This allows the maintenance personnel to correct any biases found. (v) Implementation, working with the models generated and data obtained in real-time [59,60].…”

Section: Rq1mentioning

confidence: 99%

A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0

et al. 2022

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The fourth industrial revolution was a milestone at the industrial level. It forced most industries to evolve technically and for their collaborators to prepare and advance together with technology; the oil industry was no exception. It develops its activities in dangerous and dynamic environments and needs to protect its human resources, equipment, and infrastructure. This article presents a scoping review, based on the PRISMA guidelines, of pipeline maintenance methodologies based on Industry 4.0. From the first collection of 123 articles from prestigious databases such as SpringerLink, MDPI, Scopus, IEEEXplore and ACM, a final sample of 31 articles was obtained. Here, technologies that enhance preventive and predictive maintenance systems are discussed. The results show that predictive maintenance compared to preventive maintenance has a percentage difference in upkeep time optimization of 38% in the last five years. This difference was corroborated with a Student’s t-test for independent samples, with a significance of 0.023. Likewise, the most used technologies were analyzed, with artificial intelligence standing out with 45.16%.

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IoT monitoring system for air quality assessment and collecting data

Cited by 5 publications

References 21 publications

Modeling and implementation of a low-cost IoT-smart weather monitoring station and air quality assessment based on fuzzy inference model and MQTT protocol

Modeling and implementation of a low-cost IoT-smart weather monitoring station and air quality assessment based on fuzzy inference model and MQTT protocol

Development of a hot weather warning tool for heat index monitoring in Thailand

A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0

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