A Reconfigurable Fire Detection Platform with Live Monitoring and Automated Actuation Based on Sensor Data Analytics

Abstract

This paper presents a robust and intelligent fire detection and response system that leverages a multi-sensor configuration that includes DHT11, MQ-2, and LDR with dynamically tunable threshold controls managed through a LabVIEW-based GUI. In contrast to conventional fire alarm systems that rely on rigid, hardcoded thresholds prone to false positives due to environmental variability or industrial maintenance, the proposed system enables real-time threshold reconfiguration without firmware modifications. Sensor data is acquired via an Arduino microcontroller and transmitted to LabVIEW, which applies a threshold-based decision logic to transmit a serial command that actuates a suite of response mechanisms. Upon fire detection, the system activates an auditory buzzer, visual LED indicators, an exhaust fan, and a camera while triggering a servo-operated emergency exit. A real-time camera feed allows human-in-the-loop verification, while a hardware emergency override ensures safe manual reset in the event of false alarms. In non-critical states, an ultrasonic proximity sensor enables automated, contactless door operation. Experimental validation showed an average response time of 2.4 seconds from the moment a sensor threshold was breached to full actuator activation. The standard deviation across trials ranged from 0.2 to 0.5 seconds, reflecting only minor variability due to ambient conditions such as temperature, humidity, light, and gas concentration. The proposed architecture enhances adaptability, reduces false triggers, and improves operational safety, rendering it highly suitable for dynamic industrial environments and intelligent building systems.