RFID-Based Automated Computer Terminal Access and Time Management System for Internet Cafes: The “CRBRS” Gaming Hub Eulogio “Amang” Rodriguez Institute of Science and Technology
Authors
Eulogio “Amang” Rodriguez Institute of Science and Technology Nagtahan St. Sampaloc, Manila (Philippines)
Eulogio “Amang” Rodriguez Institute of Science and Technology Nagtahan St. Sampaloc, Manila (Philippines)
Eulogio “Amang” Rodriguez Institute of Science and Technology Nagtahan St. Sampaloc, Manila (Philippines)
Eulogio “Amang” Rodriguez Institute of Science and Technology Nagtahan St. Sampaloc, Manila (Philippines)
Eulogio “Amang” Rodriguez Institute of Science and Technology Nagtahan St. Sampaloc, Manila (Philippines)
Eulogio “Amang” Rodriguez Institute of Science and Technology Nagtahan St. Sampaloc, Manila (Philippines)
Article Information
DOI: 10.47772/IJRISS.2026.100600969
Subject Category: Computer Science
Volume/Issue: 10/6 | Page No: 13747-13758
Publication Timeline
Submitted: 2026-06-19
Accepted: 2026-06-24
Published: 2026-07-09
Abstract
This study presents the design, implementation, and empirical evaluation of the CRBRS Gaming Hub, a hardware-software integrated system engineered to resolve the operational inefficiencies inherent in manual time-tracking and billing practices within Philippine internet cafe environments. The system architecturally couples an RFID-enabled identification layer—implemented via the MFRC522 reader module interfaced to an Arduino Uno microcontroller—with a dual-service backend orchestrated inside an Electron main process. This backend splits responsibilities between two standalone child processes: server.js, an Express REST API server bound strictly to localhost:5000 to eliminate external network exposure and suppress Windows Firewall interruptions; and bridge.js, a dedicated hardware monitoring service that communicates with the Arduino via Serial Port using a Readline Parser (@serialport/parser-readline) to prevent input/output blocking during concurrent session transactions.
Session security is enforced through a native operating system process management mechanism. Upon application launch, the Electron IPC handler captures the native Process Identifier (PID) of each spawned user process and registers it in an active session array. At the precise moment a user’s account balance reaches ₱0.00, the system executes a deep-tree forceful termination command (taskkill /PID <target> /T /F) to eliminate all associated processes without exception.
Quantitative evaluation conducted in a controlled laboratory environment simulating the concurrent session load of a standard internet cafe demonstrated that reducing manual operational dependency from 85% to 50% yielded a measurable improvement in system efficiency—from a baseline index of 0.05 to 0.45. The system achieved a mean authentication response time of 1.4 seconds and demonstrated 100% reliability in automated terminal lockout events triggered by zero-balance conditions. All financial parameters in this study are denominated in Philippine Pesos (₱), establishing a standard billing rate of ₱20.00 per hour, tiered in 15-minute increments at ₱5.00 per tier.
These findings substantiate the technical and operational viability of the CRBRS Gaming Hub as a professional, low-cost solution for automating user authentication, session management, and real-time billing in Philippine internet cafe operations.
Keywords
NA
Downloads
References
1. Ali, M., Ul Hassan, S., & Raza, M. (2022). Multi-factor authentication in RFID-based access control: A comparative security analysis. Journal of Information Security and Applications, 65, 103–118. https://doi.org/10.1016/j.jisa.2022.103118 [Google Scholar] [Crossref]
2. Cammarano, A., Varriale, V., Michelino, F., & Caputo, M. (2022). Blockchain as enabling factor for implementing RFID and IoT technologies in VMI: A simulation on the Parmigiano Reggiano supply chain. Operations Management Research, 16, 726–754. https://doi.org/10.1007/s12063-022-00324-1 [Google Scholar] [Crossref]
3. Jannah, N. F., Pratama, H. P., & Fuada, S. (2024). IoT-based smart door selector for double security: Integration of RFID and Blynk app for economical solution. Eduvest – Journal of Universal Studies, 4(10), 8580–8591. https://doi.org/10.59188/eduvest.v4i10.39011 [Google Scholar] [Crossref]
4. Khan, R., Ahmad, S., & Hussain, T. (2020). Web-based automated billing systems in commercial environments: Accuracy, efficiency, and resource optimization. International Journal of Computer Applications, 175(12), 22–28. [Google Scholar] [Crossref]
5. Li, S., Visich, J. K., Khumawala, B. M., & Zhang, C. (2006). Radio frequency identification technology: Applications, technical challenges, and strategies. Sensor Review, 26(3), 193–202. https://doi.org/10.1108/02602280610675474 [Google Scholar] [Crossref]
6. Okubanjo, A., Okandeji, A., Osifeko, O., Onasote, A., & Olayemi, M. (2022). Development of a hybrid RFID and biometric-based library management system. Gazi University Journal of Science, 35(2), 567–584. https://doi.org/10.35378/gujs.834087 [Google Scholar] [Crossref]
7. Roberts, C. M. (2006). Radio frequency identification (RFID). Computers & Security, 25(1), 18–26. https://doi.org/10.1016/j.cose.2005.12.003 [Google Scholar] [Crossref]
8. Shanthamallu, U. S., Spanias, A., Tepedelenlioglu, C., & Stanley, M. (2021). A review of deep learning algorithms and architectures for IoT applications. IEEE Access, 9, 36000–36026. [Google Scholar] [Crossref]
9. Want, R. (2006). An introduction to RFID technology. IEEE Pervasive Computing, 5(1), 25–33. [Google Scholar] [Crossref]
10. Zhao, L., Wang, X., & Chen, Y. (2019). Scalable RFID-based access management in institutional environments. Future Generation Computer Systems, 92, 436–445. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
- Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
- Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
- Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
- Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet