IoT-Based Air Quality Monitoring with Adaptive Filtering and RPA-Based Decision Automation

Low-cost air quality sensors enable dense monitoring networks but suffer from significant measurement noise and instability particularly in dynamic environments. Conventional fixed-window smoothing reduces noise but introduces a trade-off between signal stability and temporal responsiveness, often attenuating short-term pollution events. This paper proposes an adaptive filtering algorithm that dynamically adjusts the averaging window size based on short-term signal variability. The method relies on real-time variance estimation to balance noise suppression and sensitivity to rapid changes without increasing computational complexity. The approach is implemented within an IoT-based monitoring framework and evaluated using parallel measurements with a certified reference device. Comparative analysis against raw data and fixed-window filtering demonstrates improved statistical accuracy and stronger temporal correlation with reference measurements. In addition, this method enhances event detection stability in threshold-based monitoring scenarios. To support automated decision-making, the filtered signal integrated into an event-driven architecture with Robotic Process Automation (RPA), enabling reliable triggering of predefined workflows. The results show that proposed adaptive filtering provides an efficient and lightweight solution for real-time signal processing on resource-constrained devices, making it suitable for large-scale deployment in environmental monitoring systems.