Lightweight IoT-edge framework with attention-enhanced GRU for closed-loop real-time indoor environmental monitoring and prediction

Accurate real-time indoor environmental monitoring and prediction is essential for proactive control to protect occupant health and comfort. While prior studies have demonstrated real-time monitoring and AI-based prediction, real-time operation is often treated as near-real-time and evaluated using stored datasets rather than as a continuously operating, closed-loop streaming inference process. To address this practical and methodological gap, this study presents the initial phase of the Information and Communication Technology-enabled indoor air (ICTAir) framework, a real-time approach that integrates lightweight Internet of Things (IoT) technologies with hybrid deep learning (DL) model for continuous monitoring and prediction under a live closed loop deployment, while maintaining superior prediction accuracy with moderate inference-time and memory requirements compared to statistical and traditional machine learning approaches. ICTAir integrates low-cost sensors, a microcontroller, and a cloud back end, and employs an attention-enhanced gated recurrent unit with a simplified attention mechanism (GRU-SAM). The proposed GRU-SAM model is benchmarked against statistical baselines, traditional machine-learning models, and deep-learning alternatives, demonstrating consistently superior predictive accuracy. With a 1-h training window and a 5-min prediction horizon, GRU-SAM achieves low error (e.g., temperature RMSE = 0.22). Calibration experiments confirm sensor reliability and indicate a marked improvement in PM 2.5 measurements after calibration. During deployment, temperature frequently exceeded 30 °C and relative humidity surpassed 60%, while PM 2.5 and PM 10 often crossed 35 and 50 μg/m 3 , respectively. Continuous operation achieved end-to-end system latency within 1 min while maintaining 97.7% streaming inference reliability. The proposed framework provides a practical, deployment-oriented foundation for future autonomous indoor environmental control systems.