Decentralized Air Quality Management using Edge AI and Real-Time Control

This research introduces a groundbreaking approach to real-time air quality monitoring and control by implementing Edge AI on an Arduino microcontroller. The system is designed to integrate an array of sensors that capture real-time air quality data, which is subsequently processed by optimized machine learning models deployed directly on the edge device. This innovative approach removes the need for cloud connectivity, significantly reducing latency, and enabling a swift response to fluctuating air quality conditions. The use of lightweight neural networks, such as Mobile Net or Efficient Net, ensures the system is highly efficient and suitable for resource-constrained devices. Real-time control algorithms, driven by AI assessments, dynamically adjust air purifiers, ventilation systems, and other mitigation measures to maintain optimal indoor air quality. This Edge AI-powered solution offers a cost-effective and energy-efficient method for enhancing air quality in real-time, with potential applications ranging from smart homes to larger public spaces. By leveraging these advanced technologies, the system not only improves accuracy and efficiency but also empowers users to take proactive measures for their health and well-being, thereby contributing to a healthier and more sustainable environment.