TY - GEN
T1 - A Low-Power IoT-Based Smart Desk Integrated with a Facial Recognition Attendance System
AU - Khapper, Krupa V.
AU - Darwish, Hiba
AU - Alhmoud, Issa W.
AU - Gokaraju, Balakrishna
AU - Islam, A. K.M.Kamrul
AU - Graves, Corey A.
PY - 2025
Y1 - 2025
N2 - Internet of Things (IoT) technology in classrooms improves efficiency, security, and interactivity by automating tasks and tracking attendance in real-time. This paper explores the application of low-power IoT technology in classrooms, focusing on designing and implementing a system that integrates a facial recognition attendance system into a smart desk infrastructure. This study develops a secure and automated attendance system that enables real-time image capture, secure transmission, and detection of known and unknown faces to streamline attendance tracking. The system integrates components such as the ESP32-WROVER with a camera and Wi-Fi at each student's desk, facilitating wireless communication. The instructor's setup is designed to ensure effective control and monitoring, contributing to the efficient management of the smart classroom system. The system includes a user-friendly graphical user interface for real-time attendance monitoring of each smart desk, allowing instructors to manage and review attendance efficiently. The system employs robust encryption methods and secure transmission protocols to ensure data security, protecting student information against unauthorized access. The system generates CSV files that log all attendance data, which can be easily exported for record keeping and analysis. Additionally, when a face is detected, an LED at the student's desk is activated, providing immediate visual confirmation. This feature enhances the smart classroom system's usability and effectiveness. The combination of real-time monitoring, secure data handling, and automated processes supports a more efficient and responsive educational environment.
AB - Internet of Things (IoT) technology in classrooms improves efficiency, security, and interactivity by automating tasks and tracking attendance in real-time. This paper explores the application of low-power IoT technology in classrooms, focusing on designing and implementing a system that integrates a facial recognition attendance system into a smart desk infrastructure. This study develops a secure and automated attendance system that enables real-time image capture, secure transmission, and detection of known and unknown faces to streamline attendance tracking. The system integrates components such as the ESP32-WROVER with a camera and Wi-Fi at each student's desk, facilitating wireless communication. The instructor's setup is designed to ensure effective control and monitoring, contributing to the efficient management of the smart classroom system. The system includes a user-friendly graphical user interface for real-time attendance monitoring of each smart desk, allowing instructors to manage and review attendance efficiently. The system employs robust encryption methods and secure transmission protocols to ensure data security, protecting student information against unauthorized access. The system generates CSV files that log all attendance data, which can be easily exported for record keeping and analysis. Additionally, when a face is detected, an LED at the student's desk is activated, providing immediate visual confirmation. This feature enhances the smart classroom system's usability and effectiveness. The combination of real-time monitoring, secure data handling, and automated processes supports a more efficient and responsive educational environment.
UR - https://dx.doi.org/10.1109/SoutheastCon56624.2025.10971263
U2 - 10.1109/southeastcon56624.2025.10971263
DO - 10.1109/southeastcon56624.2025.10971263
M3 - Conference contribution
BT - 2025 IEEE SoutheastCon, SoutheastCon 2025
ER -
