INDOOR AIR QUALITY MANAGEMENT USING RASPBERRY PI AND GRAPHLAB

Somdeep Acharyya, Sayan Hazra, Atanu Pal, Nabarun Sengupta

Abstract: Indoor air pollution is a matter of serious concern at present times. Indoor air pollution or indoor air quality degradation is becoming an issue of discussion among scientists and environmentalists in today’s days. As human beings generally spend most of their time indoors, having good indoor air quality and its real time information is critical for maintaining human health and productivity. According to the EPA, indoor air even in centrally air conditioned buildings is more polluted than outdoor air, due to improperly maintained ventilation systems. Hence it’s an utmost necessity for human beings. Many reports have come out showing how much harmful is Indoor air quality pollution and the bad effects and diseases caused by such deterioration in the indoor air quality. Many solutions have been devised to tackle this air abuse and reduce its effects, also several methods of analyzing the quality of air and further monitoring through specialized sensors, the quality of air inside our household rooms like bedrooms or drawing rooms, even in classrooms and halls. But each of the other works have their own limitations and restraints, some are gathering data only from the classrooms or hall rooms. Nowhere there was any implementation of such measuring system in a bedroom, drawing room. In this research work, we have proposed a quick and easy solution of monitoring the air quality using dedicated sensors by connecting them to a microcontroller. The entire setup is desired to be placed inside classrooms full of students as well as in household rooms having people in them, at various times of the day with different temperature and humidity conditions. The data collected from the classrooms and drawing rooms or bedrooms under several environmental instances are to be uploaded to a server, from where it would be retrieved into our machines. After this collection of data, this data to be analyzed using some machine learning tool to invigilate the attributes of the air present in the rooms, namely the concentration of CO2, CO, the quantity of dust particles and their relative variations with changes in temperature or humidity or the number of persons present inside the room. Thus we have developed (1) a prototype to measure the indoor air pollutant concentration (2) a wireless sensor network architecture with a microcontroller (3) a data analyzing algorithm using a sophisticated but popular machine learning tool. Advantages of this prototype is that it was of very low cost and measurement was effective, also the presentation of the data was not at all robust but easy to view and monitor.

Keywords: Raspberry pi, CO2, CO, dust, Sensors, temperature and humidity, IAQM, Zigbee, Python, Graphlab

DOI: https://doi.org/10.15623/ijret.2017.0609007