Predictive Vigilance: Harnessing Internet of Things and Machine Learning for Smoke Detection

The increasing prevalence of fire incidents in various environments underscores the critical need for advanced smoke detection systems. This paper presents a novel approach, termed 'Predictive Vigilance', which integrates Internet of Things (IoT) sensors with state-of-the-art machine learning techniques to enhance smoke detection capabilities. By leveraging the inter-connectedness of IoT devices and the computational power of machine learning algorithms, a system can achieve real-time monitoring and predictive analysis of smoke presence. We propose a framework that combines sensor data acquisition, preprocessing, feature extraction, and classification using machine learning models. Through extensive experimentation and validation, we demonstrate the effectiveness and efficiency of our approach in detecting smoke accurately while minimizing false alarms. Furthermore, we discuss potential applications and implications of Predictive Vigilance in diverse settings, including residential, commercial, and industrial environments. Overall, this paper contributes to the advancement of proactive fire safety measures by harnessing the synergy between IoT technology and machine learning for smoke detection.