Simulation and Construction of an Internet of Things (IOT) Based Smart Home System with Real-Time Gas Leakage Detection

Abstract

Gas leakage poses serious threats to human safety and property, as many existing detection systems are either limited to local alarms or lack real-time remote monitoring capabilities which fail to notify users who are away or provide control over connected appliances. This study presents the simulation and construction of a WiFi-monitored smart home device with real time gas leakage detection alerts. The system integrates gas sensors (MQ-5 and MQ-7), an ESP32 microcontroller, an Organic Light Emitting Diode (OLED) display, a buzzer, and IoT cloud connectivity to provide real-time detection and monitoring of hazardous gases such as methane, Liquefied Petroleum Gas (LPG), and carbon monoxide. The hardware and software designs were developed using Proteus 8.2 Professional and Arduino Integrated Development Environment (IDE), with the ESP32 configured as a Wi-Fi Access Point hosting a local web server and dashboard. Results of simulation and hardware tests show that the system successfully detected gas leaks, displayed concentration levels, and triggered a local alarm (buzzer). This confirmed stable power performance, efficient communication, and high reliability in both simulated and practical environments. The Response times were fast (<2 seconds), validating the system’s reliability for real-time safety monitoring. The system’s user-friendly interface, low-cost design, and autonomous operation make it a viable solution for modern smart homes. Its contribution brings a new architecture that enables fully offline monitoring through a locally hosted web server, ensuring low latency and enhanced deployment flexibility which extends to improving safety standards, enabling remote monitoring, and providing a foundation for integrating predictive safety features in future IoT applications. Future studies should focus on the Integration of artificial intelligence or machine learning algorithms for predictive gas leakage detection.