Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
LLMs and IoT: A Comprehensive Survey on Large Language Models and the Internet of Things
0
Zitationen
8
Autoren
2026
Jahr
Abstract
The Internet of Things (IoT) has emerged as a technological cornerstone, enabling highly interconnected systems that integrate billions of diverse devices. The combined data output of these devices is estimated at hundreds of exabytes per day. The rise of Large Language Models (LLMs) presents new opportunities for managing these devices, processing the data they generate, and addressing the technological and societal challenges posed by IoT ecosystems. However, the integration of LLMs into IoT also introduces significant challenges. This article offers the most comprehensive survey to date on the integration of LLMs into IoT ecosystems. We cover various technical layers, including devices, software engineering, sensing, networking, data processing, privacy and security, and human interaction.We synthesize insights from more than 300 articles, identify open research gaps, and highlight promising future directions, ranging from semantic reasoning and communication to the integration of quantum computing. By addressing the characteristics, challenges, and opportunities associated with LLM integration, this article serves as a foundational resource for researchers and practitioners aiming to enhance IoT functionalities with LLMs while navigating the associated complexities.
Ähnliche Arbeiten
Federated Learning: Challenges, Methods, and Future Directions
2020 · 4.471 Zit.
Deep Learning: Methods and Applications
2014 · 3.321 Zit.
Mobile Edge Computing: A Survey on Architecture and Computation Offloading
2017 · 2.916 Zit.
Machine Learning: An Artificial Intelligence Approach
2013 · 2.639 Zit.
Machine learning and deep learning
2021 · 2.387 Zit.