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RFID-Driven Smart Border Passing Architecture for Cloud-Integrated Vehicle Authentication |
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| รหัสดีโอไอ | |
| Creator | Aran Asavanarakul |
| Title | RFID-Driven Smart Border Passing Architecture for Cloud-Integrated Vehicle Authentication |
| Contributor | Prach Asavanarakul, Nikorn Kaewpraek, Thanat Sooknuan, Sirorat Chanhom, Kamonrat Perinkul |
| Publisher | Rajamangala University of Technology Lanna |
| Publication Year | 2569 |
| Journal Title | RMUTL Engineering Journal |
| Journal Vol. | 11 |
| Journal No. | 1 |
| Page no. | 64-75 |
| Keyword | smart border passing, radio frequency identification: RFID, vehicle identification, cloud database, UHF tag, IoT-based monitoring, real-time authentication |
| URL Website | https://engsystem.rmutl.ac.th/journal/ |
| ISSN | 3027-7426 |
| Abstract | As global trade and tourism continue to expand, cross-border vehicle traffic has significantly increased, demanding more efficient and secure inspection systems. Conventional border checkpoint operations in Thailand rely on manual document verification, which is time-consuming and error-prone. This study proposes the design and implementation of a smart border passing system using Radio Frequency Identification (RFID) to enhance the identification and monitoring of vehicles at border checkpoints. The proposed system integrates three main components: a portable RFID reader, a flexible RFID tag sticker, and a cloud-based database dashboard for data synchronization and tracking. The RFID reader was developed as a compact, battery-powered device capable of reading UHF tags and transmitting data via Wi-Fi in real time. Experimental results demonstrate that the reader achieved 100% detection accuracy within 1.8 meters, with reliable tag readability for 30 days under real outdoor conditions. The web-based dashboard successfully displayed ENTRY, EXIT, and TIMEOUT events, enabling efficient vehicle tracking and administrative reporting. The system offers a practical and cost-effective solution for smart border management, reducing manual workload and supporting proactive detection of unauthorized crossings or vehicle theft. Future enhancements will focus on improving adhesive durability, expanding wireless communication, and integrating predictive analytics for real-time anomaly detection. |
