让大模型与物联网设备顺畅对话的标准化协议
IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol
- 用边缘服务器实现大模型与物理设备的统一通信协议
- 任务成功率100%，响应仅205毫秒，内存占用74KB
- 适合想快速对接大模型与物联网系统的开发者
大语言模型（LLMs）与物联网（IoT）系统融合面临硬件异构和控制复杂等挑战。模型上下文协议（MCP）作为关键使能技术，提供大模型与物理设备间的标准化通信。本文提出IoT-MCP框架，通过边缘部署的服务器实现MCP，连接大模型与物联网生态。为支持严格评估，我们构建了IoT-MCP Bench基准测试，包含114个基础任务（如“当前温度是多少？”）和1,140个复杂任务（如“我感觉太热了，你有什么建议？”）。在22种传感器类型和6种微控制器上实验验证，IoT-MCP实现100%的任务成功率，生成工具调用完全符合预期，结果准确无误，平均响应时间为205毫秒，峰值内存占用74KB。本工作开源了集成框架（https://github.com/Duke-CEI-Center/IoT-MCP-Servers），并提供了大模型-物联网系统标准化评估方法。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) with Internet-of-Things (IoT) systems faces significant challenges in hardware heterogeneity and control complexity. The Model Context Protocol (MCP) emerges as a critical enabler, providing standardized communication between LLMs and physical devices. We propose IoT-MCP, a novel framework that implements MCP through edge-deployed servers to bridge LLMs and IoT ecosystems. To support rigorous evaluation, we introduce IoT-MCP Bench, the first benchmark containing 114 Basic Tasks (e.g., ``What is the current temperature?'') and 1,140 Complex Tasks (e.g., ``I feel so hot, do you have any ideas?'') for IoT-enabled LLMs. Experimental validation across 22 sensor types and 6 microcontroller units demonstrates IoT-MCP's 100% task success rate to generate tool calls that fully meet expectations and obtain completely accurate results, 205ms average response time, and 74KB peak memory footprint. This work delivers both an open-source integration framework (https://github.com/Duke-CEI-Center/IoT-MCP-Servers) and a standardized evaluation methodology for LLM-IoT systems.
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