Improving the Detection and Warning Fire System on the Smart Campus Area using ANFIS

Abstract

 The adoption of IoT technology in universities has significantly improved campus security,
emergency response, and control systems, particularly through IoT-based surveillance and fire detection
systems. Traditional fire detection systems suffer from high false alarm rates, leading to unnecessary
resource allocation. This paper proposes an intelligent fire detection system leveraging IoT and adaptive
fuzzy systems to enhance accuracy and reduce false alarms. The system uses sensors to collect real-time
data, which is analyzed by an Adaptive Neuro-Fuzzy Inference System (ANFIS) to determine alarm levels
based on input severity. ANFIS combines fuzzy logic and neural networks, enabling learning and
adaptation. Data analyzed by ANFIS is sent to ThingSpeak channels for real-time monitoring. When a fire
is confirmed, alerts with fire location, timestamp, and severity are sent via SMS through GSM to the fire
management system. The system utilizes cost-effective, small-sized sensors, ensuring repeatable solutions.
After training, the ANFIS system achieved 99.59% accuracy, significantly reducing false alarms. It
demonstrated faster detection times by reducing the fire detection rate by 28% and maintaining high
detection accuracy with fewer sensors, training inputs, and epochs. Utilizing IoT and ANFIS technologies,
the system integrates efficiency, speed, and reliability, protecting lives and property and highlighting the
importance of safety and technology in serving humanity.