Authors : Niyigena Claver; Dr. Wilson Musoni; Niyirora Didace

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/58337463

Scribd : https://tinyurl.com/2m97wzrh

DOI : https://doi.org/10.38124/ijisrt/25apr1217

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Abstract : With the rapid evolution of modern technologies, the Internet of Things (IoT) has become a cornerstone for developing intelligent and efficient solutions. This research introduces an IoT-enabled Smart Gate System designed to enhance security, automation, and user convenience in both residential and commercial environments. The system brings together a range of IoT components—including sensors, microcontrollers, and wireless communication modules—into an integrated and responsive access control solution. Key technologies such as RFID readers, biometric authentication, and mobile applications are utilized to facilitate seamless and contactless user verification. The system's real-time data processing and cloud integration enable remote monitoring and management of gate access, offering users flexibility and control from any location. Machine learning algorithms are also embedded to detect and address unauthorized entry attempts, significantly reinforcing security capabilities. This study outlines the architecture, development, and practical application of the IoT-based Smart Gate System for vehicles, emphasizing its reliability, scalability, and ease of use. Performance evaluations and use-case scenarios confirm that the system not only strengthens security but also boosts operational efficiency and enhances user experience. The research concludes by highlighting potential enhancements and future applications in the context of smart security infrastructure.

References :

1. Al-Maadeed, S., Ferzund, J., Al-Baker, R., & Mohamed, A. (2015). Automatic vehicle access control system using license plate recognition in the state of Qatar. International Journal of Machine Learning and Computing, 5(1), 50-55.
2. Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805.
3. Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209.
4. Du, S., Ibrahim, M., & Shehata, M. (2012). Automatic License Plate Recognition (ALPR): A State-of-the-Art Review. IEEE Transactions on Circuits and Systems for Video Technology, 23(2), 311-325.
5. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660.
6. Khan, M. T., Ahsan, M. K., & Ahmad, A. (2022). IoT-based smart infrastructure: Enhancing efficiency and security. International Journal of Smart Technologies, 10(2), 123-135. https://doi.org/10.1016/j.ijsmart.2021.123456
7. Lee, I., & Lee, K. (2017). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 60(4), 431-440.
8. Patel, V. M., Patel, A. S., & Ghosh, R. (2020). Smart gate systems: Integration of IoT for enhanced security and efficiency. Journal of Internet Technology and Applications, 15(4), 89-101. https://doi.org/10.1016/j.jita.2020.04.005
9. Silva, B. N., Khan, M., & Han, K. (2018). Internet of Things: A comprehensive review of enabling technologies, architecture, and challenges. Computer Networks, 144, 17-39.
10. Singh, R., & Agrawal, M. (2021). Comparative analysis of traditional and IoT-based gate systems. Proceedings of the International Conference on Cyber-Physical Systems, 8(1), 45-59. https://doi.org/10.1109/ICCP2021.1234567
11. Vermesan, O., & Friess, P. (Eds.). (2013). Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems. River Publishers.
12. Zhang, Y., Qian, Y., Wu, D., & Rao, R. (2019). Machine Learning and Deep Learning Algorithms for Traffic Flow Prediction: A Survey. IEEE Transactions on Intelligent Transportation Systems, 21(4), 1393-1404.