A Low-Cost Autonomous Multi-Sensor Fusion System for Real-Time Fire Detection and Suppression in Smart Environments
Fire hazards pose a significant threat to human safety and environmental sustainability, particularly in densely populated and infrastructure-critical regions. This paper presents the design and experimental evaluation of a low-cost autonomous fire detection and suppression system based on a multi-sensor fusion approach, aimed at smart and sustainable environments. The proposed system integrates flame, smoke (MQ-2), temperature (DHT22), and ultrasonic sensors to enable reliable fire detection, environmental monitoring, and autonomous navigation. A decision-based sensor fusion algorithm is employed to minimize false alarms and improve detection robustness under varying conditions. The system is capable of autonomously locating fire sources and performing targeted suppression using a servo-controlled water nozzle. Experimental validation conducted across multiple indoor scenarios demonstrates a detection accuracy of 91.2%, an average response time of 2.4 s, and a 30% reduction in false alarms compared to single-sensor methods. The proposed solution offers a cost-effective and scalable approach for early-stage fire response and can be extended to IoT-enabled smart safety systems for sustainable infrastructure.