AI-enabled low-powered wireless area networks for quality air

Due to the serious impact it has on both human health and the environment, poor air quality has attracted increasing amount of attention in recent years. Traditional air quality monitoring devices often have drawbacks including heavy power use and restricted coverage. To address these challenges, this study proposes an innovative approach utilizing AI-enabled low-powered wireless area networks (LPWANs) for quality air monitoring. The suggested system uses artificial intelligence (AI) approaches to monitor air quality metrics in real-time, such as particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon monoxide (CO), and ozone (O₃). A dense network can be formed by deploying low-power wireless sensors in strategic locations to ensure comprehensive coverage of the monitored area. AI plays a critical role in this system by providing intelligent data processing and analysis capabilities. Machine learning algorithms predict air quality trends, detect anomalies, and identify potential pollution sources. By continuously learning from the collected data, the system can adapt to changing environmental conditions and improve its accuracy over time. The low-power aspect of the proposed system is achieved through the adoption of energy-efficient wireless communication protocols and sensor technologies. This ensures prolonged battery life for the deployed sensors, reducing the need for frequent maintenance and minimizing the overall operational costs. In addition, the gathered data on air quality may be sent to a central server for additional analysis and visualization. This enables environmental agencies, policymakers, and the general public to access real-time and historical air quality information, facilitating informed decision-making and raising awareness about maintaining a healthy living environment. The effectiveness of the proposed AI-enabled low-powered WANs for quality air monitoring is evaluated through a series of experimental tests and comparisons with existing systems. The results demonstrate the system’s capability to provide accurate and timely air quality information while consuming minimal power.