International Journal of Engineering and Technology

Indoor air quality monitoring and analytics is one of the important interdisciplinary research areas, which is attracting significant attention of various researchers from environment, mathematics, material science and electrical and computer engineering. According to a research study conducted by World Health Organization (WHO), pollution of indoor air is the most known hazardous case for respiratory diseases such as lung cancer, asthma and chronic diseases. Lack of information about the pollution sources and its serious impact on health leads to a huge number of people likely to be affected by various types of respiratory diseases. With the recent developments in sensing technology, machine learning and communication technology, IoT based Smart Real Time Indoor air quality sensing and analytics have been implemented to promote better awareness for users to alert them about indoor air quality to maintain the wellbeing in their indoor environments. The paper provides a proof of concept on IoT based Indoor air quality sensing system and analytics. The data is collected for analyzing indoor air quality in various indoor settings such as kitchen for oily based cooking, living room for insecticide spray, and smoking and flour mill for detecting flour dust during crop grinding. We used J48 and Naïve Bayes machine learning algorithm to model the air quality status. Result shows that the Naïve Bayes Algorithm detects 99.11% and J48 algorithm detects 99.30 % accurately