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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

13

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

168527-168545

Kód UT WoS článku

001586205100003

EID výsledku v databázi Scopus

2-s2.0-105017700555