将大模型引入物联网，提升决策与上下文理解能力。
When IoT Meet LLMs: Applications and Challenges
- 融合大模型与边缘/雾/云计算，优化资源利用与实时处理。
- 提出基于大模型集体智能的工业物联网预测维护新架构。
- 系统梳理物联网与大模型结合的关键挑战与研究方向。
大语言模型（LLMs）的进展正高效重塑多个领域的流程。物联网（IoT）是其重要应用潜力领域，整合可提升决策与系统交互能力。本文探索了LLM在物联网中的多重角色，尤其关注其推理能力。研究表明，LLM-IoT融合能实现多种场景下的高级决策与上下文理解。进一步分析了LLM与边缘、雾、云计算范式的协同，展示其在优化资源利用、增强实时处理及提供可扩展解决方案方面的优势。据我们所知，这是首个系统性研究边缘-雾-云三类架构下物联网-大模型融合的综述。此外，提出一种面向工业物联网的新系统模型，利用基于大模型的集体智能实现预测性维护与状态监控。最后，指出关键挑战与开放问题，为未来研究提供洞见。
原文摘要 · Abstract (English)
Recent advances in Large Language Models (LLMs) have positively and efficiently transformed workflows in many domains. One such domain with significant potential for LLM integration is the Internet of Things (IoT), where this integration brings new opportunities for improved decision making and system interaction. In this paper, we explore the various roles of LLMs in IoT, with a focus on their reasoning capabilities. We show how LLM-IoT integration can facilitate advanced decision making and contextual understanding in a variety of IoT scenarios. Furthermore, we explore the integration of LLMs with edge, fog, and cloud computing paradigms, and show how this synergy can optimize resource utilization, enhance real-time processing, and provide scalable solutions for complex IoT applications. To the best of our knowledge, this is the first comprehensive study covering IoT-LLM integration between edge, fog, and cloud systems. Additionally, we propose a novel system model for industrial IoT applications that leverages LLM-based collective intelligence to enable predictive maintenance and condition monitoring. Finally, we highlight key challenges and open issues that provide insights for future research in the field of LLM-IoT integration.
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