Edge AI-Driven Air Quality Monitoring and Notification System: A Multilocation Campus Perspective

With a growing emphasis on environmental health and safety, monitoring and managing air quality in large-scale settings such as campuses are becoming increasingly critical. This research proposes an innovative approach that integrates multilocation Internet of Things (IoT) sensors, edge artificial intelligence (AI), machine learning, and public API integration to create a comprehensive air quality monitoring and notification system for campus environments. Our framework deploys IoT sensors across various locations within the campus to collect real-time data on air quality parameters. Leveraging edge AI capabilities, these sensors process data locally, enabling rapid analysis and anomaly detection without the need for centralized processing. Furthermore, machine learning algorithm is used to analyze the collected data, identify patterns, and predict air quality. To enhance user accessibility and engagement, the system use public APIs to deliver notifications and alerts regarding air quality status.