FIRE BAE Advanced IoT System for Fire Prevention and Real-Time Telegram Notifications

Fire incidents threaten life and property, necessitating effective prevention and protection systems. The background highlights that traditional fire detection methods often lack real-time notification capabilities, leading to delayed responses. Despite advancements, existing solutions require extensive calibration and are susceptible to environmental factors, underscoring the need for a more reliable and user-friendly system. The objective of this research is to develop "FIRE BAE" (Fire Intelligence Rapid Expertly Based Application Easily), an IoT-based system for early fire detection and notification via Telegram. The methodology integrates flame and MQ-2 gas sensors with an ESP32-CAM microcontroller to provide immediate local and remote alerts. The system was tested in various environments, including an IT room and a library, to evaluate its accuracy, response time, and reliability. Sensor sensitivity was calibrated, and data was collected at 1-second intervals over multiple 30-minute test sessions. The results showed a high detection accuracy of 95%, a false positive rate of 2%, and an average response time of 3 seconds, indicating the system’s reliability and promptness in alerting users to potential fire hazards. In conclusion, the integration of advanced sensors, real-time data processing, and user-friendly notifications positions FIRE BAE as a significant advancement in fire detection and prevention technology. Despite some limitations related to environmental factors and Wi-Fi dependency, the system effectively addresses critical gaps in traditional methods. Future enhancements will focus on improving sensor calibration, exploring alternative connectivity options, and reducing false positives.