An Intelligent Gas Leakage Monitoring and Protection System Using Wireless Sensor Networks and Lightweight Deep Learning

Gas leakage poses a serious safety threat in homes, industries, and commercial settings, where it can cause explosions, fires, and health hazards. Early detection is therefore critical to reducing these risks. This paper presents an improved system for monitoring and preventing gas leakage by combining wireless sensor networks with intelligent data analysis. A gas sensor continuously measures the surrounding gas concentration and generates time-based data rather than a single reading. A lightweight deep learning model then analyzes this data and learns both normal and abnormal gas patterns, enabling the system to detect early leakage before it reaches a dangerous level while reducing false alarms caused by environmental noise and fluctuations. When the model detects a leakage risk, the controller activates a ventilation fan, an alarm buzzer, and a gas shut-off valve. The system transmits gas data to a remote unit through ZigBee communication and sends a notification message via GSM technology. The proposed system improves reliability and response time compared with conventional threshold-based detectors. It also lowers false-alarm rates while keeping the hardware simple and energy-efficient, and the intelligent layer strengthens safety-related decisions. The results show that integrating a lightweight deep learning model with a wireless sensor network produces an accurate, responsive, and energy-efficient gas monitoring solution. This makes the proposed system suitable for real-world gas leakage detection and protection applications.