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Hostelhub: Streamline Accommodation Management System

Dhanshri Vyavahare, Tanvi Tayawade, Pratiksha Shinde, Ashutosh Garad

Abstract


This project aims to demonstrate the prototype of IoT-enabled biometric attendance system utilizing ESP32 microcontrollers and R305 fingerprint modules. The system securely captures fingerprint data from multiple users and transmits it to a web server via Wi-Fi, enabling real-time attendance tracking. Fingerprint enrollment and verification occur on the server-side, utilizing the fingerprint sensor module and a web-based interface developed in PHP, CSS, and JavaScript. The technology replaces manual procedures and guarantees accuracy and efficiency in attendance tracking. Through the online interface, administrators may view attendance records, user information, and arrival and departing times. Data can be readily exported to Excel for analysis and reporting needs.


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References


Adeoye, O. S. (2010). A multi-modal biometric approach to prevent examination malpractices in Nigerian educational institutions. International Journal of Computer Applications, 4(7), 20–26. https://doi.org/ [If available].

Santoso, B., & Sari, M. W. (2019). Development of a student attendance system utilizing Internet of Things (IoT) technology. Journal of Physics: Conference Series, 1254, 012064. https://doi.org/ [If available].

Ettah, E., Ushie, P., Ekah, U., & Eze, B. (2021). An analysis of noise island distribution on the campus of Cross River University of Technology, Calabar, Nigeria. Journal of Scientific and Engineering Research, 8(6), 1–7.

Emeruwa, C., & Ekah, U. (2022). Enhancement of the equation error model algorithm for active noise control. Journal of Multidisciplinary Engineering Science and Technology, 9(1), 15067–15072.

Srivastava, H. (2013). A comparative study of biometrics for human identification. IOSR Journal of Computer Engineering, 15(1), 22–29.

Gagandeep, Arora, J., & Kumar, R. (2019). IoT-based biometric fingerprint attendance system. In Innovations in Computer Science and Engineering (pp. 523–530). Springer. https://doi.org/ [If available].

Yusof, Y. W. M., Mohd Nasir, M. A., Othman, K. A., Suliman, S. I., Shahbudin, S., & Mohamad, R. (2018). Real-time Internet-based attendance system using face recognition technology. International Journal of Engineering & Technology, 7(3.15), 174–178.

Salim, O. A. R., Olanrewaju, R. F., & Balogun, W. A. (2018). Class attendance management system utilizing face recognition. In Proceedings of the 7th International Conference on Computer and Communication Engineering (ICCCE) (pp. 93–98). Kuala Lumpur, Malaysia. https://doi.org/ [If available].

Bhattacharya, S., Nainala, G. S., Das, P., & Routray, A. (2018). Smart Attendance Monitoring System (SAMS): A face recognition-based attendance solution for classroom settings. In Proceedings of the 18th IEEE International Conference on Advanced Learning Technologies (ICALT) (pp. 358–360). Mumbai, India. https://doi.org/ [If available].

Soniya, V., Swetha, S. R., Swetha, T. K., Ramakrishnan, R., & Sivakumar, S. (2017). Automating attendance using biometric face recognition. In Proceedings of the International Conference on Power and Embedded Drive Control (ICPEDC) (pp. 122–127). Chennai, India. https://doi.org/ [If available].


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