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Article type: Research Article
Authors: Ramachandraarjunan, Senthilkumara; * | Perumalsamy, Venkatakrishnanb | Narayanan, Balajic
Affiliations: [a] Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamilnadu, India | [b] Department of Electronics and Communication Engineering, CMR Technical Campus, Telagana, India | [c] Department of Information Technology, Karpagam College of Engineering, Coimbatore, Tamilnadu, India
Correspondence: [*] Corresponding author. Senthilkumar Ramachandraarjunan, Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamilnadu, India. E-mail: sentinfo@gmail.com.
Abstract: Monitoring indoor air quality stays needed for human health because people use more than 95% of air in their indoor rooms. An Intelligent Internal Air Quality Monitoring (IIAQM) system built on the Internet of Things (IoT) devices has been developed and tested in Quantanics Techserv Private Limited, Tamilnadu, India. To monitor the levels of CO2, PM2.5 (Particle Matters 2.5), and moisture measurement, the IIAQM model has been used to monitor the present level of air quality. The gateway collects IIAQM sensor data in a few seconds and transfers data to cloud server. Approved users can incorporate the cloud systems through mobile applications or web servers. Installation of sensor networks, instrument transformers, and IoT-powered microcontrollers will provide air quality monitoring for buildings. The proposed window controller configuration is designed with the help of a Recurrent Neural Network (RNN) to predict the air quality level in advance. If the air quality level is above the normal level, the window controller automatically will open with the help of sensor activity control system. After the AQI (Air Quality Index) becomes normal, hence the window controller is closed automatically. The air quality index, CO2, and humidity data are visualized on the Grafana dashboard.
Keywords: Internet of things, machine learning, recurrent neural networks humidity sensor, intelligent internal air quality monitoring system
DOI: 10.3233/JIFS-212955
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2853-2868, 2022
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