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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2507.17865 |
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| _version_ | 1866913958572589056 |
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| author | Kalita, Alakesh |
| author_facet | Kalita, Alakesh |
| contents | The convergence of Large Language Models (LLMs) and Internet of Things (IoT) networks open new opportunities for building intelligent, responsive, and user-friendly systems. This work presents an edge-centric framework that integrates LLMs into IoT architectures to enable natural language-based control, context-aware decision-making, and enhanced automation. The proposed modular and lightweight Retrieval Augmented Generation (RAG)-based LLMs are deployed on edge computing devices connected to IoT gateways, enabling local processing of user commands and sensor data for reduced latency, improved privacy, and enhanced inference quality. We validate the framework through a smart home prototype using LLaMA 3 and Gemma 2B models for controlling smart devices. Experimental results highlight the trade-offs between model accuracy and inference time with respect to models size. At last, we also discuss the potential applications that can use LLM-based IoT systems, and a few key challenges associated with such systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17865 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Talk with the Things: Integrating LLMs into IoT Networks Kalita, Alakesh Networking and Internet Architecture The convergence of Large Language Models (LLMs) and Internet of Things (IoT) networks open new opportunities for building intelligent, responsive, and user-friendly systems. This work presents an edge-centric framework that integrates LLMs into IoT architectures to enable natural language-based control, context-aware decision-making, and enhanced automation. The proposed modular and lightweight Retrieval Augmented Generation (RAG)-based LLMs are deployed on edge computing devices connected to IoT gateways, enabling local processing of user commands and sensor data for reduced latency, improved privacy, and enhanced inference quality. We validate the framework through a smart home prototype using LLaMA 3 and Gemma 2B models for controlling smart devices. Experimental results highlight the trade-offs between model accuracy and inference time with respect to models size. At last, we also discuss the potential applications that can use LLM-based IoT systems, and a few key challenges associated with such systems. |
| title | Talk with the Things: Integrating LLMs into IoT Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2507.17865 |