Preprocessing and Framework for Edge Intelligence in Air Quality Control: Work on Progress

As urban areas face increasing challenges related to air pollution, the integration of edge computing and artificial intelligence (AI) offers a viable solution for real-time monitoring and control. This research focuses on the development of preprocessing techniques that enhance data collection and analysis at the edge, addressing critical issues such as energy consumption and data latency. By proposing a lightweight framework, the study aims to facilitate intelligent processing and data sharing in air quality applications. Additionally, the paper highlights the significance of real-time data in managing indoor air quality (IAQ) and its impact on occupant health and productivity. The ongoing work seeks to create a comprehensive framework that not only improves air quality monitoring but also incorporates predictive analytics to forecast air quality trends, thereby informing policy and operational decisions. Ultimately, this research underscores the potential of edge intelligence to transform air quality management into a proactive and adaptive system, contributing to healthier urban living environments.