Technical Solution for a Real-Time Air Quality Monitoring System

Simo, Attila; Dziţac, Simona; Frigura-Iliasa, Flaviu Mihai; Muşuroi, Sorin; Andea, Petru; Meianu, Dragoș

doi:10.15837/ijccc.2020.4.3891

Cited by 6 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aside from this, various papers were released on the creation of forecasting algorithms using neural networks, artificial intelligence, and advanced learning methodologies to provide building residents with an early warning of dangerous pollutant concentrations. In addition, the authors released a systematic review to emphasize the contributions already established scholars in the subject, as well as the gaps in knowledge [17]. The researchers gathered present data from 4 separate countryside and urban sites on six significant IAQ parameters, namely, PM 10 , PM 25 , CO 2 , CO, NO 2 , as well as two critical thermal convenience factors, humidity and temperature.…”

Section: Introductionmentioning

confidence: 99%

Real-time Air Quality Monitoring in Smart Cities using IoT-enabled Advanced Optical Sensors

Aserkar,

Godla,

El-Ebiary

et al. 2024

IJACSA

View full text Add to dashboard Cite

Air quality control has drawn a lot of attention from both theoretical research and practical application due to the air pollution problem's increasing severity. As urbanization accelerates, the need for effective air quality monitoring in smart cities becomes increasingly critical. Traditional methods of air quality monitoring often involve stationary monitoring stations, providing limited coverage and outdated data. This study proposes an Internet of Things (IoT) centred framework equipped with inexpensive devices to monitor pollutants vital to human health, in line with World Health Organization recommendations, in response to the pressing issue of air pollution and its increased importance. The hardware development entails building a device that can track significant contamination percentages. Ammonia, carbon monoxide, nitrogen dioxide, PM2.5 and PM10 particulate matter, ozone, and nitrogen dioxide. The gadget is driven by the ESP-WROOM-32 microcontroller, which has Bluetooth and Wi-Fi capabilities for easy data connection to a cloud server. It uses PMSA003, MICS-6814, and MQ-131 sensors. The gadget activates indicators when a pollutant concentration exceeds the allowable limit, enhancing its software to enable immediate response and intervention. This work leverages the robust cloud architecture of Amazon Web Server (AWS) to integrate it into the system and improve accessibility and data control. This combination no longer just ensures data preservation but also enables real-time tracking and analysis, which adds to a comprehensive and preventive strategy for reducing air pollution and preserving public health. With an RMSE score of 3.7656, the Real-Time Alerts with AWS Integration model-which was built in Python-has the lowest value.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-time Air Quality Monitoring in Smart Cities using IoT-enabled Advanced Optical Sensors

Aserkar,

Godla,

El-Ebiary

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…IoT has an important role to save energy and time in doing work leading to the work efficiency [10,11]. IoT can be applied to various sectors because it can work as a monitor and controller [12,13].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of LoRaWAN Class A and Class C in the Measurement of Nutrient Content Systems

2023

IJIES

View full text Add to dashboard Cite

This research was evaluated the measurement of the nutrient contents of Nitrogen (N), Phosphorus (P), and Potassium (K) in the soil using Antares LR201 microcontroller board and LoRaWAN class A and class C networks with ANTARES platform as the network server. Later the data was monitored and controlled using an Android application which aims to compare the two types of networks. In this study, several tests were carried out, namely SNR, RSSI, delay, and packet loss. The test was carried out in the city of Bandung with different LoRa signal strength that could be seen on Telkom IoT web. Based on experiment, LoRaWAN class C has higher QoS performance for delay, SNR, RSSI, and packet loss than LoRaWAN class A for test location distance of 0, 1, 2, and 4 km away from BTS LoRa. According to the ITU-T G.1010 standard, the delay value produced in this experiment is in the "Very good" category, packet loss value is in the Very good "category", and the RSSI value is in the "Enough" category. And the SNR value is in the recommended category of SF10. Also, it can be concluded that the redder LoRa coverage location map reveals, the better QoS value obtained. In testing the sending and receiving of downlink data in terms of delay, according to the downlink data mechanism in class A, downlink data can be sent if the uplink data has been sent. While class C downlink data can be sent at any time.

show abstract

“…In recent years, several studies have proposed air-quality-monitoring mechanisms [6][7][8][9][10]. Most platforms are formed by a wireless-based sensors network, which measures different points and can display the information in real time or store it for future consultation.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Analysis of Outdoor Carbon Dioxide Concentration by Autonomous Sensors

et al. 2022

View full text Add to dashboard Cite

Countless problems have been caused by the excessive emission of polluting gases, such as carbon dioxide (CO2), into the atmosphere. Therefore, more effective monitoring of CO2 is essential, especially in central or industrial regions. Thus, this work shows the development of a platform for monitoring the CO2 concentration, which is composed of autonomous and independent sensors that have their own energy source, storage capacity, and data replication for the central server. To validate the platform, CO2 measurements were taken at three strategic points in an outdoor environment in a Brazilian urban center. This platform proved to be an evolution over another system previously proposed by the group that was based on the use of a wireless network of sensors to monitor CO2. This new project managed to overcome limitations that compromised the efficiency of the first platform, which were mainly related to the interference in the communication signals between the network sensors due to the existence of physical barriers in the monitoring environment. With that, this new platform showed greater security in the maintenance of collected data and allowed for the expansion of the physical complexity of the environments that can be monitored.

show abstract

Technical Solution for a Real-Time Air Quality Monitoring System

Cited by 6 publications

References 10 publications

Real-time Air Quality Monitoring in Smart Cities using IoT-enabled Advanced Optical Sensors

Real-time Air Quality Monitoring in Smart Cities using IoT-enabled Advanced Optical Sensors

Performance Analysis of LoRaWAN Class A and Class C in the Measurement of Nutrient Content Systems

Monitoring and Analysis of Outdoor Carbon Dioxide Concentration by Autonomous Sensors

Contact Info

Product

Resources

About