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Real-time and predictive analytics of air quality with IoT system: a review

Environmental pollution particularly due to the emission of combustible gas from industry, haze, and vehicles, that has always been a major concern. Continuous monitoring of the air quality is hence essential to ensure early precaution or preventive measure can be taken in eliminating potential heal...

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Main Authors: Osman, N., Jamlos, M. F.
Format: Conference or Workshop Item
Published: 2021
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/96109/
http://dx.doi.org/10.1007/978-981-33-4597-3_11
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id my.utm.96109
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spelling my.utm.961092022-07-03T08:25:37Z http://eprints.utm.my/id/eprint/96109/ Real-time and predictive analytics of air quality with IoT system: a review Osman, N. Jamlos, M. F. TK Electrical engineering. Electronics Nuclear engineering Environmental pollution particularly due to the emission of combustible gas from industry, haze, and vehicles, that has always been a major concern. Continuous monitoring of the air quality is hence essential to ensure early precaution or preventive measure can be taken in eliminating potential health risk which may be done via Smart Environmental Monitoring system with the Internet of Things (IoT), which is cost-effective and efficient way to control air pollution and curb climate change, IoT applications along with Machine Learning(ML) can make the data prediction in real-time. ML can be used to predict the previous and current data obtained by sensors. This review describes the existence of an integrated research field in the development of the environmental monitoring system and ML method. The findings of this review interestingly show that (i) various communication module is used for environmental monitoring system. (ii) Very less integration of IoT together with predictive analytics, it is separately to study for air pollution monitoring system. (iv) Data analytics for Air Pollution Index (API) prediction along with IoT, with various communication protocols can assist in the development of real-time, and continuous high precision environmental monitoring systems. (v) Machine Learning (ML) Regression algorithm is suitable for prediction and classification of concentration gas pollutant, while ANN and SVM algorithm is used for forecasting. 2021 Conference or Workshop Item PeerReviewed Osman, N. and Jamlos, M. F. (2021) Real-time and predictive analytics of air quality with IoT system: a review. In: Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020, 6 August 2020, Gambang. http://dx.doi.org/10.1007/978-981-33-4597-3_11
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Osman, N.
Jamlos, M. F.
Real-time and predictive analytics of air quality with IoT system: a review
description Environmental pollution particularly due to the emission of combustible gas from industry, haze, and vehicles, that has always been a major concern. Continuous monitoring of the air quality is hence essential to ensure early precaution or preventive measure can be taken in eliminating potential health risk which may be done via Smart Environmental Monitoring system with the Internet of Things (IoT), which is cost-effective and efficient way to control air pollution and curb climate change, IoT applications along with Machine Learning(ML) can make the data prediction in real-time. ML can be used to predict the previous and current data obtained by sensors. This review describes the existence of an integrated research field in the development of the environmental monitoring system and ML method. The findings of this review interestingly show that (i) various communication module is used for environmental monitoring system. (ii) Very less integration of IoT together with predictive analytics, it is separately to study for air pollution monitoring system. (iv) Data analytics for Air Pollution Index (API) prediction along with IoT, with various communication protocols can assist in the development of real-time, and continuous high precision environmental monitoring systems. (v) Machine Learning (ML) Regression algorithm is suitable for prediction and classification of concentration gas pollutant, while ANN and SVM algorithm is used for forecasting.
format Conference or Workshop Item
author Osman, N.
Jamlos, M. F.
author_facet Osman, N.
Jamlos, M. F.
author_sort Osman, N.
title Real-time and predictive analytics of air quality with IoT system: a review
title_short Real-time and predictive analytics of air quality with IoT system: a review
title_full Real-time and predictive analytics of air quality with IoT system: a review
title_fullStr Real-time and predictive analytics of air quality with IoT system: a review
title_full_unstemmed Real-time and predictive analytics of air quality with IoT system: a review
title_sort real-time and predictive analytics of air quality with iot system: a review
publishDate 2021
url http://eprints.utm.my/id/eprint/96109/
http://dx.doi.org/10.1007/978-981-33-4597-3_11
_version_ 1738510324810645504
score 13.211869

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