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In urban areas, exposure to indoor air pollution is expanding because of numerous reasons, including the construction of more tightly sealed buildings, reduced ventilation, the use of synthetic materials for building and furnishing and the use of chemical products, pesticides, and household care products. Indoor air pollution can start inside the building or be attracted from outside. Other than nitrogen dioxide, carbon monoxide, and lead, there are various different toxins that influence the air quality in an encased space. The most susceptible groups to indoor pollution are women and children because women lung sizes are significantly smaller to male counterparts and lung volume to body volume proportion of children is significantly higher than adults. Indoor air pollution monitoring requires equal attention as outdoor pollution. With advent in sensor technology and studies showing harmful effect of indoor air pollution it is important for us to start monitoring the air quality inside our schools, offices, hospitals, home and other places. The nature of air is influenced by multi-dimensional elements including area, time, and unverifiable factors. As of late, numerous specialists started to utilize the big data investigation approach because of headways in big data applications. Sensors build on powerful Arduino board and wi-fi networking units are tested to monitor air quality of three parameters; suspended particles, organic vapors and humidity. These key parameters are monitored over period of time, the time series data is stored in cloud service, and machine learning is applied to find ways to predict and manage air quality. The paper presents the IoT device architecture, cloud application architecture and sample results for an indoor test environment. Mobile and web-based visualizations were created for the data collected from the sensors. An alarm system is also developed to notify the user when the air quality deteriorates to unhealthy level.
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