Development of an IoT-Based Environmental Monitoring, Prediction and Control System for Carbon-Momoxide and Liquefied Petroleum Gas (LPG)

Abstract

Air pollutants pose significant threats to environmental and human health, making air quality monitoring essential for ensuring safety in various environments. This study presents the development and implementation of an Internet of Things (IoT)-based environmental pollutant monitoring system utilizing a network of sensors (MQ-2 and ESP01) integrated with an Arduino microcontroller. The system was analyzed using the Object-Oriented Approach and Unified Modeling Language (UML) notation symbols for clear design representation. A Prophet Model was employed as the prediction model, enabling the system to forecast pollutant levels based on historical data. The system was designed to monitor and analyze air quality in real-time, measuring pollutants such as carbon monoxide (CO) and liquefied petroleum gas (LPG) based on the Parts per Million (PPM) metric. It also featured a control mechanism for CO levels by triggering alerts and mitigation processes when thresholds were exceeded, while effectively detecting LPG leaks. Data collected were logged to a remote server and updated over the internet, ensuring accessibility via any smart mobile device through cloud integration. This system fits within the broader category of IoT-based Environmental and Safety Monitoring Systems. This IoT-based system provides a scalable, efficient solution for air quality monitoring and control, contributing to improved environmental safety.