用大模型实现实时物联网数据搜索，精准匹配服务需求。
Agentic Search Engine for Real-Time IoT Data
- 结合大模型与检索增强生成技术，理解复杂查询意图。
- 在多设备场景下实现92%的服务意图识别准确率。
- 适合需要实时决策的智慧城市、智能运维等场景。
物联网（IoT）使各类设备能够联网通信，但系统碎片化限制了数据共享与协同管理。我们此前提出SensorsConnect框架，借鉴万维网理念，实现协作式物联网中的内容与传感器数据无缝共享。本文介绍物联网代理搜索引擎（IoT-ASE），一种专为物联网环境设计的实时搜索系统。IoT-ASE利用大语言模型（LLMs）和检索增强生成（RAG）技术，应对海量实时物联网数据的搜索挑战，可处理复杂查询并返回准确、上下文相关的结果。我们在多伦多实施了一个应用场景，展示如何通过实时物联网数据提升服务推荐质量。评估表明，IoT-ASE在识别意图驱动的服务方面达到92%的准确率，输出内容简洁、相关且具上下文感知，优于Gemini等通用系统。这凸显了其在使实时物联网数据可访问、支持高效实时决策方面的潜力。
原文摘要 · Abstract (English)
The Internet of Things (IoT) has enabled diverse devices to communicate over the Internet, yet the fragmentation of IoT systems limits seamless data sharing and coordinated management. We have recently introduced SensorsConnect, a unified framework to enable seamless content and sensor data sharing in collaborative IoT systems, inspired by how the World Wide Web (WWW) enabled a shared and accessible space for information among humans. This paper presents the IoT Agentic Search Engine (IoT-ASE), a real-time search engine tailored for IoT environments. IoT-ASE leverages Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) techniques to address the challenge of searching vast, real-time IoT data, enabling it to handle complex queries and deliver accurate, contextually relevant results. We implemented a use-case scenario in Toronto to demonstrate how IoT-ASE can improve service quality recommendations by leveraging real-time IoT data. Our evaluation shows that IoT-ASE achieves a 92\% accuracy in retrieving intent-based services and produces responses that are concise, relevant, and context-aware, outperforming generalized responses from systems like Gemini. These findings highlight the potential IoT-ASE to make real-time IoT data accessible and support effective, real-time decision-making.
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