IoT-Enabled Temperature Pattern Classification Model for Sustainable Applications

This study presents the design and implementation of an IoT-based environmental monitoring system that integrates real-time data collection with classification analytics to support sustainable decision-making. In response to the increasing need for accessible and affordable monitoring tools, the system utilizes a NodeMCU ESP8266 microcontroller, paired with DHT22 and raindrop sensors, to capture temperature, humidity, and rainfall status at 30-second intervals. Data is transmitted wirelessly and stored on Google Sheets, enabling cloud-based visualization and analysis. A Random Forest classifier was applied to categorize temperature conditions into low, medium, and high ranges based on humidity and rain status to derive actionable insights from the collected data. Model performance produces overall accuracy of 65.9% revealed a strong ability to detect high-temperature conditions, with rain status identified as the most influential predictor. However, challenges such as class imbalance and limited prediction of low-temperature conditions were observed. Recommendations include enhancing the model with balanced datasets, time-based feature engineering, and considering regression models for more granular forecasting. This system demonstrates a scalable and adaptable approach to environmental monitoring, suitable for educational, research, and field applications in data-driven sustainability efforts.