Energy-Efficient Green IoT System for Real-Time Air Quality Monitoring of PM<sub>1</sub>–PM<sub>10</sub>

Particulate matter (PM₁, PM_2.5, and PM₁₀) represents a critical environmental and occupational health concern, particularly in industrial environments where airborne dust frequently exceeds safe thresholds. Prolonged exposure contributes to respiratory, cardiovascular, and neurological disorders, underscoring the need for effective, continuous monitoring systems. This study aims to develop an energy-efficient Green Internet of Things (Green IoT) framework for real-time monitoring of air quality using low-cost sensor nodes designed to operate sustainably in industrial settings. The system integrates ESP32-S3 microcontrollers, Wi-Fi connectivity, and a data deduplication algorithm that reduces redundant transmissions to optimize power consumption. Three sensor nodes were deployed at a feed mill facility in Lampung, Indonesia, to evaluate performance under real operational conditions. During 24-hour continuous monitoring, each node generated 1,270–1,418 valid data points while achieving an approximately 20% reduction in unnecessary transmissions. Pearson correlation analysis showed strong intra-node consistency (r = 0.94–1.00) and moderate correlation (r ≈ 0.5) between nodes, validating reliability for localized particulate assessment. The results demonstrated that the proposed system effectively captured spatiotemporal variations in particulate concentrations while maintaining low energy use, making it suitable for scalable deployment in industrial environments. The findings contribute to the advancement of Green IoT by demonstrating a practical approach to sustainable communication and data management in distributed sensor networks. Research implications include the potential integration of this framework into large-scale environmental surveillance systems and the development of adaptive algorithms for predictive monitoring using machine learning. Practically, the system offers an affordable and replicable solution to support regulatory compliance, occupational health management, and environmental protection. Socially, the implementation of continuous, transparent air quality monitoring promotes community awareness and supports policy initiatives aimed at reducing industrial pollution. Future work will explore long-term deployments and advanced analytics for predictive air quality forecasting.