Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm
This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsines...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/15/4956 |
| _version_ | 1797525219624091648 |
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| author | Chew Cheik Goh Latifah Munirah Kamarudin Ammar Zakaria Hiromitsu Nishizaki Nuraminah Ramli Xiaoyang Mao Syed Muhammad Mamduh Syed Zakaria Ericson Kanagaraj Abdul Syafiq Abdull Sukor Md. Fauzan Elham |
| author_facet | Chew Cheik Goh Latifah Munirah Kamarudin Ammar Zakaria Hiromitsu Nishizaki Nuraminah Ramli Xiaoyang Mao Syed Muhammad Mamduh Syed Zakaria Ericson Kanagaraj Abdul Syafiq Abdull Sukor Md. Fauzan Elham |
| author_sort | Chew Cheik Goh |
| collection | DOAJ |
| description | This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO<sub>2</sub>, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (<i>R</i><sup>2</sup>). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an <i>R</i><sup>2</sup> of 0.9981. |
| first_indexed | 2024-03-10T09:09:41Z |
| format | Article |
| id | doaj.art-f7f5302dee104c7293910f961a260b2a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:09:41Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f7f5302dee104c7293910f961a260b2a2023-11-22T06:08:17ZengMDPI AGSensors1424-82202021-07-012115495610.3390/s21154956Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction AlgorithmChew Cheik Goh0Latifah Munirah Kamarudin1Ammar Zakaria2Hiromitsu Nishizaki3Nuraminah Ramli4Xiaoyang Mao5Syed Muhammad Mamduh Syed Zakaria6Ericson Kanagaraj7Abdul Syafiq Abdull Sukor8Md. Fauzan Elham9Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, MalaysiaFaculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, MalaysiaAdvanced Sensor Technology, Centre of Excellence (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau 02600, MalaysiaGraduate Faculty of Interdisciplinary Research, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, JapanFaculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, MalaysiaGraduate Faculty of Interdisciplinary Research, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, JapanFaculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, MalaysiaFaculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, MalaysiaAdvanced Sensor Technology, Centre of Excellence (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau 02600, MalaysiaSelangor Industrial Corporation Sdn Bhd, Seksyen 14, Shah Alam 40000, MalaysiaThis paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO<sub>2</sub>, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (<i>R</i><sup>2</sup>). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an <i>R</i><sup>2</sup> of 0.9981.https://www.mdpi.com/1424-8220/21/15/4956internet of things (IoT)machine learning predictionin-vehicle air qualitysmart mobilitysmart city |
| spellingShingle | Chew Cheik Goh Latifah Munirah Kamarudin Ammar Zakaria Hiromitsu Nishizaki Nuraminah Ramli Xiaoyang Mao Syed Muhammad Mamduh Syed Zakaria Ericson Kanagaraj Abdul Syafiq Abdull Sukor Md. Fauzan Elham Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm Sensors internet of things (IoT) machine learning prediction in-vehicle air quality smart mobility smart city |
| title | Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm |
| title_full | Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm |
| title_fullStr | Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm |
| title_full_unstemmed | Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm |
| title_short | Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm |
| title_sort | real time in vehicle air quality monitoring system using machine learning prediction algorithm |
| topic | internet of things (IoT) machine learning prediction in-vehicle air quality smart mobility smart city |
| url | https://www.mdpi.com/1424-8220/21/15/4956 |
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