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Sensors 2015, 15(5), 11665-11684; doi:10.3390/s150511665

Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN)

1
Center of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Taman Muhibbah, Jejawi, 02600 Arau, Perlis, Malaysia
2
Faculty of Engineering Technology, Universiti Malaysia Perlis (UniMAP), Kampus UniCITI Alam, 02100 Sungai Chuchuh, Padang Besar, Perlis, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 19 January 2015 / Revised: 21 April 2015 / Accepted: 22 April 2015 / Published: 20 May 2015
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [3427 KB, uploaded 20 May 2015]   |  

Abstract

Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity. View Full-Text
Keywords: indoor air quality; artificial neural network (ANN); pattern recognition indoor air quality; artificial neural network (ANN); pattern recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Saad, S.M.; Andrew, A.M.; Shakaff, A.Y.M.; Saad, A.R.M.; Kamarudin, A.M.Y.; Zakaria, A. Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN). Sensors 2015, 15, 11665-11684.

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