Development of fuzzy logic based online indoor air quality monitoring system with distributed sensor systems architecture

Indoor air quality generally refers to the environmental characteristics within the building that can affect human health, comfort and performance. Unlike the outside air, the indoor air is recycled continuously which causes the pollutants to be collected and stored. Poor indoor air quality has bad effects on human health in short and long-term. Considering the long hours people spend in indoor environments, it is necessary to be informed about the pollution which is not aware of by continuously monitoring indoor air quality. Thus, the right strategies for the development and protection of clean air required for healthy living can be offered. Therefore, many studies are being carried out on indoor air quality monitoring systems which are a smart city application (green building) in both academic and commercial environments. In this study, the proposed indoor air quality monitoring system aims to evaluate all comfort parameters in an environment together and to provide the best air quality for the people in the environment. In this context, unlike studies in the literature, a flexible and scalable indoor air quality monitoring system with web-based, distributed architecture that allows multi-parameter measurement independent of the measurement environment has been proposed. In addition, a fuzzy model was proposed to enable the analysis of independent measurement parameters (CO2 level and thermal comfort value) from indoor environments and to obtain indoor environmental quality information. In the study, indoor air quality and thermal comfort of the classrooms in the first floor of the Faculty of Computer and Information Sciences of Sakarya University has been monitored by web based real-time monitoring system under various scenarios. Environmental factors (temperature, humidity, airflow rate, CO2 concentration) are detected by measuring devices in the system. Then, the raw information obtained is evaluated together with subjective factors such as metabolic rate and clothing level. Thus, thermal comfort value and indoor air quality information of the environment are obtained. Finally, the acquired indoor air quality and thermal comfort information are analyzed by fuzzy logic method and then the end user is informed about the general environmental quality of the classes.