Good air quality is critical to healthy and productive life in the indoor environment. Quality of air inside buildings depends on several factors. Till now, it was not possible to measure and monitor indoor air quality (IAQ) through mobile phones and wireless sensors in real time, as standardized miniature sensors were not commercially available. With the introduction of wireless air quality sensors that can pair with most contemporary smartphones, it is now possible to monitor indoor environment pervasively in real time, take precautionary measures, and maintain a healthy life. In this work, we have created an IoT sensing system to monitor and analyse the time variation of carbon dioxide in a university classroom. Using the sensed data, forecast models have been developed to predict the build-up of carbon dioxide, an indicator of IAQ. © Springer Nature Singapore Pte Ltd. 2020.

Divya Lohani

Computer Science and Engineering

(SoE) School of Engineering

Rastogi K.

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

An Internet of Things Framework to Forecast Indoor Air Quality Using Machine Learning

Communications in Computer and Information Science

A working person on an average spends 1.5–2 h every day traveling either to their places of work or for other daily activities, using metros, trams, buses, and cars, as common modes of travel. Most of such commuters regularly suffer from health conditions like headache, breathless condition, drowsiness, etc. Numerous accidents have been reported due to drowsiness while driving, which may occur due to the build-up of carbon-dioxide (CO2) build in the vehicle chamber. This paper attempts to monitor, analyze, and predict air quality inside the vehicle. This work proposes a sensing system using an off-the-shelf sensor Sensordrone which is connected to an Android Smartphone using Bluetooth Low Energy. The data obtained from the proposed sensing system are then utilized to perform predictive analysis of CO2 build-up inside the vehicular chamber using Auto Regressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR). Root-Mean-Square Error for SVR and ARIMA models is 47.91 ppm and 55.32 ppm CO2, respectively, indicating that SVR outperformed ARIMA in predicting the CO2 build-up inside the vehicle. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.

Journal of Reliable Intelligent Environments

Modeling vehicle indoor air quality using sensor data analytics

Building and Environment

CO2 tracer gas concentration decay method for measuring air change rate

IEEE Sensors Journal

ISSAQ: An Integrated Sensing Systems for Real-Time Indoor Air Quality Monitoring

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '14 Adjunct

Indoor air quality monitoring system for smart buildings

Environmental Health Perspectives

Is CO2an Indoor Pollutant? Direct Effects of Low-to-Moderate CO2Concentrations on Human Decision-Making Performance

2012 Sixth International Conference on Sensing Technology (ICST)

Indoor air quality monitoring using wireless sensor network