Internet of Things for Indoor Air Quality Monitoring

2021 ◽  
Author(s):  
Jagriti Saini ◽  
Maitreyee Dutta ◽  
Gonçalo Marques
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Quality Monitoring ◽  
Air Quality Monitoring

scholarly journals Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

2020 ◽  
Vol 17 (14) ◽  
pp. 4942 ◽  
Author(s):  
Jagriti Saini ◽  
Maitreyee Dutta ◽  
Gonçalo Marques
Keyword(s):  
Systematic Review ◽  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Well Being ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Research Knowledge

Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015–2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO2, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

scholarly journals A Cost-Effective Air Quality Supervision Solution for Enhanced Living Environments through the Internet of Things

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 170 ◽  
Author(s):  
Gonçalo Marques ◽  
Rui Pitarma
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Real Time ◽  
Indoor Air ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Living Environments

We spend about 90% of our lives in indoor living environments. Thus, it is essential to provide indoor air quality monitoring for enhanced living environments. Advances in networking, sensors, and embedded devices have made monitoring and supply of assistance possible to people in their homes. Technological advancements have made possible the building of smart devices with significant capabilities for sensing and connecting, but also provide several improvements in ambient assisted living system architectures. Indoor air quality assumes an important role in building productive and healthy indoor environments. In this paper, the authors present an Internet of Things system for real-time indoor air quality monitoring named iAir. This system is composed by an ESP8266 as the communication and processing unit and a MICS-6814 sensor as the sensing unit. The MICS-6814 is a metal oxide semiconductor sensor capable of detecting several gases such as carbon monoxide, nitrogen dioxide, ethanol, methane, and propane. The iAir system also provides a smartphone application for data consulting and real-time notifications. Compared to other solutions, the iAir system is based on open-source technologies and operates as a totally Wi-Fi system, with several advantages such as its modularity, scalability, low cost, and easy installation. The results obtained are very promising, representing a meaningful contribution for enhanced living environments as iAir provides real-time monitoring for enhanced ambient assisted living and occupational health.

scholarly journals THE MONITORING SYSTEM OF INDOOR AIR QUALITY BASED ON INTERNET OF THINGS

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Agung Pangestu ◽  
Muhammad Yusro ◽  
Wisnu Djatmiko ◽  
Ariep Jaenul
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Monitoring System ◽  
Indoor Air ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Test Results ◽  
Dust Level ◽  
The Internet Of Things

The indoor air quality monitoring system is needed to find out good air condition. Good air condition can be seen from two (2) factors, namely dust, and temperature. Dust in a room can affect health if it exceeds the threshold of 0.15 mg/m3, and the temperature of 35oC has been determined by SK MENKLH No.02/MENKLH/I/1998. For that, we need a system that can determine the temperature and dust conditions in a room. The main objective of this research is to create an indoor air quality monitoring system based on the Internet of Things (IoT). This research uses engineering methods, which include planning, design, testing, and system implementation. In this system, when the dust level is more than 0.15 mg/m3 the LED indicator 1 and the active sprayer tell and neutralize the dust content in the air and when the intensity of the temperature is more than 35oC the LED indicator 2 and the active sprayer tell and neutralize the temperature intensity at the room. When both values exceed the set threshold, the LED indicators 1, LED 2, buzzer, the sprayer will be active simultaneously to notify and neutralize the air and temperature in the room. The test results show this system can work well with the percentage of errors in the testing of 12% for dust sensors and 1.6% for temperature sensors.

scholarly journals Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 184
Author(s):  
Rafia Mumtaz ◽  
Syed Mohammad Hassan Zaidi ◽  
Muhammad Zeeshan Shakir ◽  
Uferah Shafi ◽  
Muhammad Moeez Malik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Ambient Conditions ◽  
Public Facilities

Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for future instances, which holds paramount importance for taking timely preemptive actions, especially for COVID-19 actual and potential patients as well as people suffering from acute pulmonary disorders and other health problems. In this regard, we have proposed an indoor air quality monitoring and prediction solution based on the latest Internet of Things (IoT) sensors and machine learning capabilities, providing a platform to measure numerous indoor contaminants. For this purpose, an IoT node consisting of several sensors for 8 pollutants including NH3, CO, NO2, CH4, CO2, PM 2.5 along with the ambient temperature & air humidity is developed. For proof of concept and research purposes, the IoT node is deployed inside a research lab to acquire indoor air data. The proposed system has the capability of reporting the air conditions in real-time to a web portal and mobile app through GSM/WiFi technology and generates alerts after detecting anomalies in the air quality. In order to classify the indoor air quality, several machine learning algorithms have been applied to the recorded data, where the Neural Network (NN) model outperformed all others with an accuracy of 99.1%. For predicting the concentration of each air pollutant and thereafter predicting the overall quality of an indoor environment, Long and Short Term Memory (LSTM) model is applied. This model has shown promising results for predicting the air pollutants’ concentration as well as the overall air quality with an accuracy of 99.37%, precision of 99%, recall of 98%, and F1-score of 99%. The proposed solution offers several advantages including remote monitoring, ease of scalability, real-time status of ambient conditions, and portable hardware, and so forth.

scholarly journals Building an indoor air quality monitoring system based on the architecture of the Internet of Things

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wen-Tsai Sung ◽  
Sung-Jung Hsiao
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Monitoring System ◽  
Indoor Air ◽  
Quality Monitoring ◽  
The Internet ◽  
Air Quality Monitoring ◽  
Environment Variables ◽  
The Internet Of Things

AbstractWith rapidly changing technology, people have more and more requirements for thermal comforts regarding indoor temperature, humidity, and wind speed, and pay more attention to air quality. Indoor air quality has serious effects on the elderly, children, and those with respiratory allergies. Based on the architecture of the Internet of Things smart home, this study constructed an indoor air quality monitoring system to explore how people can live in an environment with good air quality. Among the numerous air quality indices (AQIs), the carbon dioxide index and AQI of the American Society of Heating, Refrigerating and Air-Conditioning Engineers are selected as the indices suitable for this study. The common points of the two indices are combined, and then, based on the data of the Environmental Protection Administration, indoor and outdoor environmental parameters are analyzed, and controllable environment variables are simulated to analyze their effects on air quality. This study designed effective load control using fuzzy control and developed a fuzzy rule base for simulation of the environment variables. Decision logic was used to replace the threshold control of indoor air quality in the past, and a comfortable air quality monitoring system was designed by combining the Arduino Uno development board and ESP8266 Wi-Fi wireless transmission modules.

scholarly journals Multi-points indoor air quality monitoring based on Internet of Things

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Zhibin Liu ◽  
Guangwen Wang ◽  
Liang Zhao ◽  
Guangfei Yang
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Quality Monitoring ◽  
Air Quality Monitoring

scholarly journals Building an Indoor Air Quality Monitoring System Based on the Architecture of the Internet of Things

2020 ◽  
Author(s):  
Wen-Tsai Sung ◽  
Sung-Jung Hsiao
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Monitoring System ◽  
Indoor Air ◽  
Quality Monitoring ◽  
The Internet ◽  
Air Quality Monitoring ◽  
Environment Variables ◽  
The Internet Of Things

Abstract With rapidly changing technology, people have more and more requirements for thermal comforts regarding indoor temperature, humidity, and wind speed, and pay more attention to air quality. Indoor air quality has serious effects on the elderly, children, and those with respiratory allergies. Based on the architecture of the Internet of Things (IoT) smart home, this study constructed an indoor air quality monitoring system to explore how people can live in an environment with good air quality. Among the numerous air quality indices (AQIs), the carbon dioxide (CO 2 ) index and AQI of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) are selected as the indices suitable for this study. The common points of the two indices are combined, and then, based on the data of the Environmental Protection Administration, indoor and outdoor environmental parameters are analyzed, and controllable environment variables are simulated to analyze their effects on air quality. This study designed effective load control using fuzzy control and developed a fuzzy rule base for simulation of the environment variables. Decision logic was used to replace the threshold control of indoor air quality (IAQ) in the past, and a comfortable air quality monitoring system was designed by combining the Arduino Uno development board and E ESP8266 Wi-Fi wireless transmission modules.

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