Enhancing Occupancy Detection Through IoT: A Comparative Analysis of Classifiers
Gregory Davrazos, George Raftopoulos, Theodor Panagiotakopoulos, Sotiris B. Kotsiantis, Achilles D. Kameas · 2024
This study presents a comprehensive comparative analysis of classifier performance for enhancing occupancy detection in Internet of Things (IoT) environments. Occupancy detection is a crucial aspect in various applications such as smart buildings, energy management, and security systems. Leveraging IoT data, we evaluate the effectiveness of different classifiers in accurately detecting occupancy. Through experimentation and analysis, we identify the strengths and limitations of various classifiers, providing insights into their suitability for real-world deployment. Our findings offer valuable guidance for selecting the most suitable classifier for occupancy detection tasks in IoT environments, ultimately contributing to improved efficiency and effectiveness in IoT-based systems.