Data Analytics for Wireless Sensor Network Based Indoor Environment Quality Management

The Growth of an organization or company depends mostly on their workers' health and the health of worker depends upon the healthy building. A healthy building may enrich workers' health, retention, decision-making and throughput. This can help you to save energy and segregate your work. The developed device monitors real-time indoor environment parameters such as CO2, CO, PM2.5, TVOC, Light intensity, Temperature, and humidity. The devices are IOT facilitated which can link data with MQTT server in JSON format. This data can be used in building management systems for power-efficient operation of all electrical devices. So, this paper presents the predicting air quality using IOT and machine learning and deep learning approaches. The result shows how much the AQI model shows the different types of indoor air pollutant as described (Co2, CO, Temperature, Humidity, PM2.5, PM10, TVOC). With the best possible number of environment monitoring sensors, this model will determine the whole building analysis. It will help to improve human health as well as overall building efficiency.