Optimization and Management of Communication Technology in Smart Education Environments
DOI:
https://doi.org/10.71222/b93b9d98Keywords:
smart education environments, communication technologies, 5G and edge computing, network optimization, load balancing, data compression, scalabilityAbstract
The advancement of communication technologies has significantly transformed smart education environments, enabling seamless connectivity, real-time interactions, and personalized learning experiences. This study explores key optimization and management strategies for communication technologies in education, focusing on real-world case studies, including Tsinghua University's 5G campus infrastructure, Zuoyebang's AI-driven cloud computing enhancements, and Zhejiang University's blockchain-based data security solutions. These cases demonstrate the effectiveness of technologies such as 5G, edge computing, AI-driven load balancing, and blockchain in addressing challenges related to network efficiency, system stability, and data security. Despite these advancements, challenges such as network congestion, security vulnerabilities, device compatibility issues, and resource limitations persist. Future research should focus on emerging technologies like 6G networks for enhanced speed and reliability, AI-driven network optimization for intelligent traffic management, and blockchain for improved data security. Sustainable solutions must also be explored to minimize the environmental impact of communication infrastructure in education. By continuously innovating and addressing these challenges, smart education systems can become more efficient, secure, and accessible, ultimately enhancing learning experiences on a global scale.
References
1. Z. Mo, "Artificial Intelligence in Lifelong Learning: Enhancing Chinese Language Instruction for Non-Native Adult Learners," GBP Proc. Ser., vol. 2, pp. 141-146, 2025, doi: 10.71222/vxzcka39.
2. B. Gros, "The design of smart educational environments," Smart Learn. Environ., vol. 3, pp. 1-11, 2016, doi: 10.1186/s40561-016-0039-x.
3. Z. T. Zhu, M. H. Yu, and P. Riezebos, "A research framework of smart education," Smart Learn. Environ., vol. 3, pp. 1-17, 2016, doi: 10.1186/s40561-016-0026-2.
4. L. Chitanana and D. W. Govender, "Bandwidth management in the era of bring your own device," Electron. J. Inf. Syst. Dev. Ctries., vol. 68, no. 1, pp. 1-14, 2015, doi: 10.1002/j.1681-4835.2015.tb00489.x.
5. C. Chowdhury, D. A. Hahn, M. R. French, E. Y. Vassermann, P. K. Manadhata, and A. G. Bardas, "eyedns: Monitoring a uni-versity campus network," in Proc. IEEE Int. Conf. Commun. (ICC), May 2018, pp. 1-7, doi: 10.1109/ICC.2018.8422082.
6. J. B. Earp and F. C. Payton, "Data protection in the university setting: Employee perceptions of student privacy," in Proc. 34th Annu. Hawaii Int. Conf. Syst. Sci., Jan. 2001, pp. 6-pp, doi: 10.1109/HICSS.2001.927152.
7. X. Xu et al., "Research on key technologies of smart campus teaching platform based on 5G network," IEEE Access, vol. 7, pp. 20664-20675, 2019, doi: 10.1109/ACCESS.2019.2894129.
8. Z. Li and Z. Ma, "A blockchain-based credible and secure education experience data management scheme supporting for searchable encryption," China Commun., vol. 18, no. 6, pp. 172-183, 2021, doi: 10.23919/JCC.2021.06.014.
9. D. M. El-Din, A. E. Hassanein, and E. E. Hassanien, "Smart environments concepts, applications, and challenges," in Machine Learn. Big Data Anal. Paradigms: Anal., Appl. and Chall., pp. 493-519, 2021. ISBN: 9783030593377.
10. A. C. Martín, C. Alario-Hoyos, and C. D. Kloos, "Smart Education: A review and future research directions," Proc., vol. 31, no. 1, p. 53, Nov. 2019, MDPI, doi: 10.3390/proceedings2019031057.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Jianfa Guo, Haibin Li (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
