Abstract:
Environment indoor air quality is a very important aspect of the health and comfort of human beings. To ensure the well-being of people, we should consider air quality in every aspect of life. Air quality becomes more challenging when it comes to indoor places overcrowded with a lot of people.
Most health care centers have enough infrastructure however; they still find it hard to ensure the good air quality for the patients. Air quality in Neonatal Intensive Care Unit (NICU) need to be monitored and maintained for the sake of the baby‟s health. Poor monitoring of air quality in NICU leads to the exposition of babies to ambient air pollutants which can affect their health status. This research proposes an IoT-based air quality monitoring and prediction in the NICU and other indoor places using fuzzy inference system that will monitor air pollutants and thermal comfort and generate a report in real-time. The system will assist in controlling the air quality parameters in the room for ensuring good conditions for the health of babies and NICU staff. A cloud server will hold all the information collected by sensors, which include the DHT11 temperature and humidity sensor, Mq7 sensor (carbon monoxide sensor), particulate matters(PM), and Mq135 (air quality sensor) to show the NICU's status for the protection of the baby‟s life. Proposed solution will be integrated with fuzzy inference system to identify, classify and predict the level of indoor air quality as well as provide a warning about poor air quality in the room.
