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

Nurmadiha, Osman and Mohd Faizal, Jamlos and Fatimah, Dzaharudin and Aidil Redza, Khan and You, Kok Yeow and Khairil Anuar, Khairi (2020) Real-time and predictive analytics of air quality with IoT system: A review. In: Recent Trends in Mechatronics Towards Industry 4.0. Springer Nature, Singapore, pp. 107-116. ISBN 978-981-33-4597-3

[img] Pdf
REAL-TIME AND PREDICTIVE ANALYTICS OF AIR QUALITY WITH IOT SYSTEM.pdf
Restricted to Repository staff only

Download (359kB) | Request a copy

Abstract

Environmental pollution particularly due to the emission of combus-tible 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 pre-caution 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 inte-grated 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 as-sist in the development of real-time, and continuous high precision environmen-tal monitoring systems. v) Machine Learning (ML) Regression algorithm is suit-able for prediction and classification of concentration gas pollutant, while ANN and SVM algorithm is used for forecasting.

Item Type: Book Chapter
Uncontrolled Keywords: Environmental pollution; monitoring system; machine learning
Subjects: T Technology > TD Environmental technology. Sanitary engineering
Faculty/Division: Institute of Postgraduate Studies
College of Engineering
Depositing User: Miss. Ratna Wilis Haryati Mustapa
Date Deposited: 06 Aug 2021 04:44
Last Modified: 06 Aug 2021 04:44
URI: http://umpir.ump.edu.my/id/eprint/31651
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item