@article{ART003332416},
author={Keun-Ho Lee},
title={A Study on the Design of an IoT-Based LLM Token Resource Management and AI Practice Support Platform},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2026},
volume={12},
number={2},
pages={103-109}
TY - JOUR
AU - Keun-Ho Lee
TI - A Study on the Design of an IoT-Based LLM Token Resource Management and AI Practice Support Platform
JO - Journal of Internet of Things and Convergence
PY - 2026
VL - 12
IS - 2
PB - The Korea Internet of Things Society
SP - 103
EP - 109
SN - 2466-0078
AB - This study proposes the design of an Internet of Things (IoT)-based LLM token resource management and AI practice support platform for the efficient operation of generative artificial intelligence-based practical education, which is rapidly expanding in recent university learning environments. Since generative AI APIs incur costs under a pay-as-you-go model and often suffer from call limitations and resource bottlenecks in large-scale concurrent usage environments, there is a growing need for a stable and sustainable educational infrastructure. To address these issues, this study designs an IoT-based platform architecture that combines a centralized token management system with a distributed user environment. The proposed system is designed to support efficient token allocation, real-time usage monitoring, and intelligent notification functions, thereby ensuring continuity in learning activities while enabling the integrated management of various LLM APIs. In addition, to strengthen data security, the platform applies API proxy-based access control and a secure isolated environment, allowing safe data utilization even in industry-academic collaboration projects. In particular, the system includes a function for quantitatively evaluating educational effectiveness by analyzing the correlation between learning outcomes and token usage. The platform proposed in this study is expected to serve as an integrated operational model that simultaneously secures cost efficiency, stability, and scalability in AI practical education, and to provide an important foundation for building university-centered AI educational infrastructure in the future.
KW - IoT;LLM;Token Resource Management;Generative AI Education;AI Practice Platform
DO -
UR -
ER -
Keun-Ho Lee. (2026). A Study on the Design of an IoT-Based LLM Token Resource Management and AI Practice Support Platform. Journal of Internet of Things and Convergence, 12(2), 103-109.
Keun-Ho Lee. 2026, "A Study on the Design of an IoT-Based LLM Token Resource Management and AI Practice Support Platform", Journal of Internet of Things and Convergence, vol.12, no.2 pp.103-109.
Keun-Ho Lee "A Study on the Design of an IoT-Based LLM Token Resource Management and AI Practice Support Platform" Journal of Internet of Things and Convergence 12.2 pp.103-109 (2026) : 103.
Keun-Ho Lee. A Study on the Design of an IoT-Based LLM Token Resource Management and AI Practice Support Platform. 2026; 12(2), 103-109.
Keun-Ho Lee. "A Study on the Design of an IoT-Based LLM Token Resource Management and AI Practice Support Platform" Journal of Internet of Things and Convergence 12, no.2 (2026) : 103-109.
Keun-Ho Lee. A Study on the Design of an IoT-Based LLM Token Resource Management and AI Practice Support Platform. Journal of Internet of Things and Convergence, 12(2), 103-109.
Keun-Ho Lee. A Study on the Design of an IoT-Based LLM Token Resource Management and AI Practice Support Platform. Journal of Internet of Things and Convergence. 2026; 12(2) 103-109.
Keun-Ho Lee. A Study on the Design of an IoT-Based LLM Token Resource Management and AI Practice Support Platform. 2026; 12(2), 103-109.
Keun-Ho Lee. "A Study on the Design of an IoT-Based LLM Token Resource Management and AI Practice Support Platform" Journal of Internet of Things and Convergence 12, no.2 (2026) : 103-109.
