FIRE-NET: Fire Detection and Assistance System Using Machine Learning

Abstract

An advanced technology-driven approach to augment fire detection and emergency response mechanisms is presented in this paper. The integration of the Internet of Things (IoT) and Machine Learning (ML) is explored for the development of an intelligent fire detection and emergency alert system. Within the proposed system, IoT encompasses a fire sensor and a GPS sensor to detect potential fire incidents and capture precise location data, respectively. Machine Learning algorithms, utilizing a curated dataset from Kaggle, are employed to analyze fire data, enhancing the accuracy of detection by minimizing false alarms. Upon confirmation of a legitimate fire incident, the system autonomously triggers an emergency response mechanism, promptly transmitting location details to multiple systems within the fire department. This seamless and automated communication channel facilitates timely intervention, minimizing widespread damage, and enhancing emergency service efficiency. The survey delves into the current state-of-the-art technologies, methodologies, and challenges associated with the integration of IoT and ML in fire detection and emergency response systems, contributing to the development of intelligent solutions for public safety improvement and the mitigation of fire-related incidents' impact on communities