Sensor Classification for CO₂ Detection in IoT-Enabled Indoor Air Quality Monitoring Systems

Indoor air quality (IAQ) is a critical factor in ensuring the health, comfort, and productivity of individuals in indoor spaces. Among various pollutants, carbon dioxide (CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>) is a primary indicator of air quality in enclosed environments. Elevated CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> levels can cause discomfort, reduced cognitive function, and even health risks when concentrations exceed safe limits. The advancement of Internet of Things (IoT) technology has significantly enhanced IAQ monitoring by enabling real-time, remote data collection, analysis, and automated control of air quality. This integration relies heavily on the use of sensors that can accurately detect CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> levels, allowing for intelligent and adaptive systems. Several types of sensors are commonly used for CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> detection in IoT-enabled IAQ monitoring systems. These sensors vary in terms of detection principle, performance, accuracy, and application suitability. This paper explores the different sensor technologies within 10 key parameters and 5 detection principles for CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> detection, focusing on their trade-off in IoT-based IAQ monitoring systems.