Paper Contents
Abstract
Gas leakage incidents pose severe threats to safety in both residential and industrial environments, often resulting in fire hazards, health risks, and significant economic losses. Traditional gas detection systems, which primarily rely on fixed threshold values and manual supervision, are inadequate in responding to subtle leaks or unattended scenarios. This paper presents a novel real-time gas monitoring and safety system that leverages ESP32 microcontrollers, SMPS-based power supply, ThingSpeak cloud integration, and Python-driven deep learning analytics to offer an intelligent, scalable, and autonomous solution. The proposed system utilizes MQ-series gas sensors (MQ2, MQ3, MQ5, MQ6, MQ7) for detecting various combustible gases, and HX710 pressure sensors for monitoring the remaining gas volume within cylinders. These sensors interface directly with an ESP32 board, which handles real-time data acquisition, local processing, and wireless communication. Unlike conventional GSM modules, this system incorporates both GSM and 5G modules for high-speed and redundant connectivity. Sensor data is transmitted securely to the ThingSpeak cloud platform, where it is stored and analyzed. The cloud system is integrated with Python-based deep learning models trained on historical leak patterns and environmental variations to enable predictive analytics, anomaly detection, and early warning capabilities. A key innovation of this system is its automated refill booking feature, which operates through a GSM-based missed call logic. When gas pressure falls below a pre-defined threshold or is predicted to run low based on trend analysis, the system autonomously triggers a refill request. Coupled with this is the use of solenoid valves enables immediate gas shutoff at the time of emergencies, preventing potential disasters. Alert mechanisms such as SMS notifications, mobile app alerts, buzzers, and visual indicators provide a comprehensive safety net that ensures user awareness and prompt response. To improve security and data integrity, the system uses encryption protocols for cloud communication and periodically updates firmware over-the-air (OTA).The system also has a built-in fallback mechanism ensures local data logging during connectivity outages, syncing to the cloud once restored. The system consumes less power compared to other proposals with the use of SMPS and battery backup for uninterrupted operation. Integration with Android applications facilitates user interaction, manual overrides, and diagnostics. Performance evaluation under controlled conditions demonstrated a leak detection accuracy of 95.3%, average alert latency of 2.8 seconds, and 98.7% success in automated refill confirmations. The proposed system achieved a 70% reduction in manual intervention and improved response times by over 60% compared to existing solutions. Additional features such as multi-sensor fusion, offline logging, battery backup, and secure cloud access enhance its adaptability across domestic and industrial contexts. The systems architecture supports future upgrades, including voice command integration and AI-based user profiling for personalized safety responses. Modular design enables scalability for large industrial deployments. For future use cases, Environmental sensors can be added to enhance context-awareness and also reduce false alarms triggered by non-gas-related conditions. The integration of AI and IoT into this safety system marks a significant step toward smart, autonomous, and predictive gas safety infrastructures.
Copyright
Copyright © 2025 Aravindhan.K. This is an open access article distributed under the Creative Commons Attribution License.
