The Role of Large Language Models in Designing Reliable Networks for Internet of Things: A Short Review of Most Recent Developments

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00388260" target="_blank" >RIV/68407700:21230/25:00388260 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/ACCESS.2025.3614246" target="_blank" >https://doi.org/10.1109/ACCESS.2025.3614246</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3614246" target="_blank" >10.1109/ACCESS.2025.3614246</a>

Result language

angličtina

Original language name

The Role of Large Language Models in Designing Reliable Networks for Internet of Things: A Short Review of Most Recent Developments

Original language description

The rapid growth of Internet of Things (IoT) networks has increased the need for intelligent, flexible, and scalable networking solutions. This paper reviews the use of Large Language Models (LLMs) to improving network protocols, automate decision-making, and strengthen security in IoT networks. A detailed analysis was conducted to classify the existing research based on applications, network types, methodologies, and performance metrics. LLMs have been used in network configuration, security monitoring, cyber threat detection, federated learning, and for improving network performance. Their integration with edge computing, 6G networks, and AI-driven network control enables real-time network adjustment, automated troubleshooting, and efficient traffic management. However, challenges such as high computing demands, high energy consumption, security risks, and slow adaptation in dynamic networks still exist. This study identifies emerging trends, including LLM-based self-learning networks, privacy-aware AI training, and hybrid AI models that combine graph-based neural networks, reinforcement learning, and multimodal AI. By reviewing recent research from 2023 to early-2025, this study provides a clear understanding of how LLMs transform the IoT and network management. The discussion highlights future research directions, focusing on decentralized AI frameworks, optimized model training, and AI-driven network automation, with the aim of developing more efficient, secure, and reliable network infrastructures.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Volume of the periodical

13

Issue of the periodical within the volume

September

Country of publishing house

US - UNITED STATES

Number of pages

19

Pages from-to

168527-168545

UT code for WoS article

001586205100003

EID of the result in the Scopus database

2-s2.0-105017700555