Next-Generation E-Library Systems: A Survey on Barcode Automation and Secure Web Architecture

Abstract

The digital transformation of academic institutions has accelerated demand for intelligent library systems that combine automation, secure access, and personalized services. This survey reviews modern E-Library architectures with emphasis on barcode-based inventory ingestion, secure dual-portal web design (admin + student), and content-aware recommendation techniques. We examine state-of-the-art approaches (barcode, RFID, API-assisted metadata ingestion, and AI recommendation), compare their strengths and shortcomings, and propose a practical system blueprint implemented with PHP/MySQL and REST APIs. We also discuss security, privacy, and scalability considerations, and outline near-term research directions such as RFID integration, blockchain audit trails, and advanced recommendation models. The paper synthesizes literature, provides design patterns, and presents a comparative evaluation to guide practitioners and researchers.

Introduction

Academic libraries are transitioning from traditional, manual, book-centered systems into intelligent digital platforms that automate operations, enhance security, and provide personalized learning support. With advancements in web technologies, barcode/RFID tools, and AI, modern E-Library systems can streamline cataloging, improve user experience, and enable data-driven management.

This paper reviews existing research and proposes a dual-portal intelligent E-Library system with separate interfaces for administrators and students. The design integrates barcode-based book ingestion, REST-based metadata fetching (Google Books/OpenLibrary), PHP–MySQL backend architecture, and a content-based recommendation module using TF–IDF and cosine similarity.

Key Components

• Automated Cataloging: Barcode scanning + external APIs reduce manual typing and improve accuracy.

• Dual-Portal Architecture: Admins manage books, users, and transactions; students search, borrow, and receive suggestions.

• Session & Concurrency Management: PHP sessions, token renewals, and optimistic locking prevent double-issue conflicts.

• Recommendation Engine: Lightweight content-based model suited for institutions lacking rating datasets.

• Security Features: Role-based access, hashed passwords, TLS, audit logs, and optional blockchain anchoring.

• Privacy Compliance: Minimal personal data storage with export/deletion capability.

Comparative Findings

The proposed barcode+API system is low-cost, efficient, and scalable, outperforming older manual or purely local systems in automation and usability. Cloud and blockchain-based systems offer advanced capabilities but involve higher cost and complexity.

Advantages

• Faster, error-free cataloging

• Cost-effective hardware/software setup

• Real-time admin–student synchronization

• Expandable PHP–MySQL backend

• AI-powered personalized book discovery

• Strong security and auditability

Disadvantages

• Requires barcode scanners/mobile camera support

• Dependent on stable network access

• Still needs librarian oversight

• AI modules increase server load

• Risk of duplicate metadata without maintenance

Future Directions

Potential upgrades include hybrid recommendation models, mobile-first designs, predictive analytics for book demand, and voice-based semantic search.

Conclusion

This survey synthesizes approaches for modern E-Library systems emphasizing barcode automation, secure dual-portal architecture, and practical personalization. The proposed blueprint balances cost, usability, and security for typical academic deployments. Future research should evaluate hybrid recommendation effectiveness and the operational trade-offs of RFID and blockchain integration.

References

[1] R. Sharma, “Digital Library Management System,” IEEE Int. Conf. on Computational Intelligence, 2019. [2] M. Patel and S. Rao, “Automation in Library with Barcode,” IJRTE, 2020. [3] A. Singh, “Cloud-based Library System,” Springer LNCS, 2021. [4] K. Lee and J. Park, “Collaborative Filtering for Book Suggestion,” ACM Digital Libraries Conf., 2022. [5] H. Zhang, “AI Text Embedding for Library Recommendation,” IEEE Access, 2023. [6] P. Jain, “User Experience in Digital Libraries,” Elsevier J. of Info. Sci., 2020. [7] T. Kumar, “RFID in Library Systems,” IOSR J. of Computer Eng., 2019. [8] J. Thomas, “AI in Education and Learning,” IEEE Trans. on Learning Technologies, 2021. [9] R. Verma, “Blockchain for Secure Library Transactions,” IEEE Blockchain Conf., 2024. [10] OpenLibrary API Documentation, 2024. [Online]. Available: https:// openlibrary.org/developers/api [11] Google Books API Documentation, 2024. [Online]. Available: https:// developers.google.com/books/docs/v1/using [12] S. Ray and A. Ahmad, “Explainable AI for Educational Systems,” IEEE Access, 2023. [13] V. Chauhan et al., “Smart Attendance Using QR and Geofencing,” IEEE ICSCDS, 2024. [14] A. Ahmed, “Smart Library Automation through AI and IoT,” IEEE SmartTech Conf., 2024. [15] M. Choi, “Cloud-Based RFID Integration for Library Automation,” Elsevier Journal on Cloud Computing, 2023. [16] N. Khan et al., “A Secure and Scalable Library Management System,” IEEE Systems Journal, 2025. [17] R. Joshi, “Decentralized Academic Recordkeeping via Blockchain,” IEEE Access, 2024. [18] L. Zhao, “AI-Powered Recommendation in Educational Repositories,” Springer Advances in Information Systems, 2025. [19] S. Narang, “PHP-Based Library Portal,” IJCSIT, 2018. [20] P. K. Singh, “Designing Secure Web Applications,” ACM WebConf, 2022.

Copyright

Copyright © 2025 Tejaswi Anil Kale, Swarupa Santosh Khaire, Sanika Uday Sonawane, Shraddha Rohidas Dhavale, Prof. Soniya Dhotre, Dr. Sandeep Kadam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.