Devi Nur Azizah, Sri Heranurweni, La Ode Muhamad Idris

Internet of Things Based Air Quality Monitoring System with Automatic Notification

Introduction

Internet of things based air quality monitoring system with automatic notification. Real-time IoT-based air quality monitoring system integrates PM2.5, PM10, CO2, NO2 sensors for automatic notifications. Enhances smart city environmental management and public awareness.

Abstract

Internet of Things (IoT)-based air quality monitoring systems represent a significant advancement in urban environmental management. This research implements a system that integrates PM2.5, PM10, CO2, and NO2 sensors for real-time monitoring of pollutants. The results showed that the integration of IoT technology with cloud computing and machine learning algorithms successfully created a responsive and accurate monitoring system. The model achieved maximum accuracy during the training process, with promising predictive capabilities in real-world implementation. The main findings of the study confirmed that the Weighted Class (WC) approach significantly improved performance in the testing and prediction process by addressing class imbalance in the dataset, while the Data Augmentation (DA) technique did not show the expected improvement due to the intrinsic characteristics of air quality data. The automatic notification system successfully provides early warnings when air quality exceeds specified thresholds, enabling proactive responses from authorities and the public. The implementation of a web-based monitoring dashboard provides comprehensive visualization of data for long-term analysis. This research contributes to the development of smart cities by providing an effective framework for air quality management, supporting data-driven decision-making, and increasing public awareness of environmental conditions.

Review

This paper presents a timely and relevant contribution to environmental management, detailing an Internet of Things (IoT)-based system for real-time air quality monitoring with an automatic notification feature. The integration of multiple crucial pollutant sensors (PM2.5, PM10, CO2, NO2) with IoT technology, cloud computing, and machine learning algorithms provides a comprehensive and modern approach to urban air quality assessment. The system's reported responsiveness, accuracy, and promising predictive capabilities, coupled with the practical implementation of automatic early warnings and a web-based monitoring dashboard, highlight its potential for significant impact on public safety and environmental policy. A key strength of the methodology lies in the thoughtful application of a Weighted Class (WC) approach, which effectively addressed class imbalance—a common challenge in real-world environmental datasets—and demonstrably improved performance in testing and prediction. However, while the abstract notes that Data Augmentation (DA) did not yield expected improvements due to "intrinsic characteristics of air quality data," a brief elaboration on these characteristics would enhance clarity and provide deeper insight into this specific finding. Further, mentioning the specific machine learning algorithms employed, beyond just "machine learning algorithms," would strengthen the technical detail provided in the abstract. Overall, this research offers a robust framework for air quality management, directly supporting the development of smart cities by enabling data-driven decision-making and fostering greater public awareness of environmental conditions. The successful implementation of an automatic notification system is particularly commendable, offering a proactive tool for authorities and citizens alike. The study lays a strong foundation for future work, perhaps in the long-term validation across diverse urban settings or the exploration of integrating additional environmental factors for even more nuanced predictive modeling.

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