Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective
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 compl...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-01-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/2/184 |
| _version_ | 1797411148652347392 |
|---|---|
| author | Rafia Mumtaz Syed Mohammad Hassan Zaidi Muhammad Zeeshan Shakir Uferah Shafi Muhammad Moeez Malik Ayesha Haque Sadaf Mumtaz Syed Ali Raza Zaidi |
| author_facet | Rafia Mumtaz Syed Mohammad Hassan Zaidi Muhammad Zeeshan Shakir Uferah Shafi Muhammad Moeez Malik Ayesha Haque Sadaf Mumtaz Syed Ali Raza Zaidi |
| author_sort | Rafia Mumtaz |
| collection | DOAJ |
| description | 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 NH<sub>3</sub>, CO, NO<sub>2</sub>, CH<sub>4</sub>, CO<sub>2</sub>, 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. |
| first_indexed | 2024-03-09T04:40:52Z |
| format | Article |
| id | doaj.art-4eb3cba93ed4496caa96ce25fd958ef2 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T04:40:52Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-4eb3cba93ed4496caa96ce25fd958ef22023-12-03T13:20:49ZengMDPI AGElectronics2079-92922021-01-0110218410.3390/electronics10020184Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 PerspectiveRafia Mumtaz0Syed Mohammad Hassan Zaidi1Muhammad Zeeshan Shakir2Uferah Shafi3Muhammad Moeez Malik4Ayesha Haque5Sadaf Mumtaz6Syed Ali Raza Zaidi7School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, PakistanSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, PakistanSchool of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley G72 0LH, UKSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, PakistanSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, PakistanDental College, HITEC-Institute of Medical Sciences, Taxila 47080, PakistanDental College, HITEC-Institute of Medical Sciences, Taxila 47080, PakistanSchool of Electronic and Electrical Engineering, University of Leeds, Leeds L2 9JT, UKIndoor 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 NH<sub>3</sub>, CO, NO<sub>2</sub>, CH<sub>4</sub>, CO<sub>2</sub>, 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.https://www.mdpi.com/2079-9292/10/2/184Internet of Things (IoT)COVID-19indoor air qualityclassificationpredictive analytic |
| spellingShingle | Rafia Mumtaz Syed Mohammad Hassan Zaidi Muhammad Zeeshan Shakir Uferah Shafi Muhammad Moeez Malik Ayesha Haque Sadaf Mumtaz Syed Ali Raza Zaidi Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective Electronics Internet of Things (IoT) COVID-19 indoor air quality classification predictive analytic |
| title | Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective |
| title_full | Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective |
| title_fullStr | Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective |
| title_full_unstemmed | Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective |
| title_short | Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective |
| title_sort | internet of things iot based indoor air quality sensing and predictive analytic a covid 19 perspective |
| topic | Internet of Things (IoT) COVID-19 indoor air quality classification predictive analytic |
| url | https://www.mdpi.com/2079-9292/10/2/184 |
| work_keys_str_mv | AT rafiamumtaz internetofthingsiotbasedindoorairqualitysensingandpredictiveanalyticacovid19perspective AT syedmohammadhassanzaidi internetofthingsiotbasedindoorairqualitysensingandpredictiveanalyticacovid19perspective AT muhammadzeeshanshakir internetofthingsiotbasedindoorairqualitysensingandpredictiveanalyticacovid19perspective AT uferahshafi internetofthingsiotbasedindoorairqualitysensingandpredictiveanalyticacovid19perspective AT muhammadmoeezmalik internetofthingsiotbasedindoorairqualitysensingandpredictiveanalyticacovid19perspective AT ayeshahaque internetofthingsiotbasedindoorairqualitysensingandpredictiveanalyticacovid19perspective AT sadafmumtaz internetofthingsiotbasedindoorairqualitysensingandpredictiveanalyticacovid19perspective AT syedalirazazaidi internetofthingsiotbasedindoorairqualitysensingandpredictiveanalyticacovid19perspective |