SMART BREATH: IOT-ENABLED OPTIMIZATION OF AIR QUALITY MONITORING AND CONTROL

Abstract

Air pollution has become a critical environmental and public health concern, demanding efficient, scalable, and real-time monitoring solutions. Traditional pollution control systems often suffer from limited spatial coverage, high costs, and delayed data acquisition, which restrict timely interventions. To overcome these challenges, this study proposes Smart Breath, an IoT-enabled framework for optimized air quality monitoring and control. The system integrates a distributed network of low-cost IoT sensors capable of measuring key pollutants such as PM2.5, PM10, CO, NO₂, and O₃, transmitting real-time data to a centralized cloud platform. Advanced optimization algorithms and machine learning models are applied to analyze pollution patterns, predict trends, and trigger automated control measures, such as adaptive ventilation, traffic regulation signals, and community alerts. The architecture emphasizes scalability, energy efficiency, and secure data transmission, making it suitable for both urban and semi-urban contexts. Prototype implementation and simulations demonstrate improved accuracy in pollutant detection, faster response times, and actionable insights for decision-makers. By combining IoT intelligence with predictive optimization, Smart Breath not only enhances situational awareness but also supports proactive pollution mitigation strategies. Ultimately, this research positions IoT-based air quality systems as a transformative tool for achieving cleaner air, healthier communities, and sustainable urban development