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Online Monitoring of Indoor Air Quality and Thermal Comfort Using a Distributed Sensor-Based Fuzzy Decision Tree Model

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Integrating IoT and AI for Indoor Air Quality Assessment

Part of the book series: Internet of Things ((ITTCC))

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Abstract

Monitoring and control of Indoor Air Quality (IAQ) have become more important, both because people spend more time indoors, especially in crowded public buildings, and because bad air has serious effects on health. Therefore, in this study, a new IAQ monitoring system is proposed that evaluates indoor comfort parameters online to provide an acceptable indoor environment for users. The online web-based, distributed, and fog computing-based monitoring system has been developed in a flexible and scalable fashion, and a distributed architecture has been used, unlike other studies. In the data processing part, a new fuzzy decision tree model is used to analyze independent measurements and environment parameters (CO2 level, thermal comfort value, number of people, and light intensity) and to obtain IAQ information. In the study, a faculty building of Sakarya University is selected as the testbed to manage case studies and to verify the model. The IAQ monitoring system has been compared with conventional systems in terms of transmission infrastructure. A fuzzy decision model has been proposed as a data processing technique as a result of comparison with fuzzy logic and Artificial Neural Networks (ANNs) under the same scenarios. The obtained results show that the proposed fuzzy decision model has 9–12% better performance than fuzzy logic and 5–7% better than ANN in the same scenarios. In addition, at the end of each case study, a survey with questions about air quality and thermal comfort has been applied to the students in the classroom. The system outputs have been compared to the survey data, and it has been observed that the proposed system produced successful results for classroom air quality.

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Author information

Authors and Affiliations

  1. Sakarya University, Faculty of Computer and Information Sciences, Department of Software Engineering, Sakarya, Turkey

    Deniz Balta & Ahmet Özmen

  2. Erciyes University, Faculty of Engineering, Department of Computer Engineering, Kayseri, Turkey

    Nesibe Yalçın

  3. Sakarya University, Faculty of Computer and Information Sciences, Department of Computer Engineering, Sakarya, Turkey

    Musa Balta

Authors
  1. Deniz Balta
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  2. Nesibe Yalçın
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  3. Musa Balta
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  4. Ahmet Özmen
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Corresponding author

Correspondence to Nesibe Yalçın .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technical Teacher’s Training and Research, Chandigarh, India

    Jagriti Saini

  2. Department of Information Management and Emerging Engineering, National Institute of Technical Teacher’s Training and Research, Chandigarh, India

    Maitreyee Dutta

  3. Polytechnic of Coimbra, Rua General Santos Costa, Oliveira do Hospital, Portugal

    Gonçalo Marques

  4. Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, VIC, Australia

    Malka N. Halgamuge

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Balta, D., Yalçın, N., Balta, M., Özmen, A. (2022). Online Monitoring of Indoor Air Quality and Thermal Comfort Using a Distributed Sensor-Based Fuzzy Decision Tree Model. In: Saini, J., Dutta, M., Marques, G., Halgamuge, M.N. (eds) Integrating IoT and AI for Indoor Air Quality Assessment. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-96486-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-96486-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96485-6

  • Online ISBN: 978-3-030-96486-3

  • eBook Packages: EngineeringEngineering (R0)

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