Large language models (LLMs) have transformed the way we interact with cyber technologies. In this paper, we study the possibility of connecting LLM with wireless sensor networks (WSN). A successful design will not only extend LLM's knowledge landscape to the physical world but also revolutionize human interaction with WSN. To the end, we present ChatTracer, an LLM-powered real-time Bluetooth device tracking system. ChatTracer comprises three key components: an array of Bluetooth sniffing nodes, a database, and a fine-tuned LLM. ChatTracer was designed based on our experimental observation that commercial Apple/Android devices always broadcast hundreds of BLE packets per minute even in their idle status. Its novelties lie in two aspects: i) a reliable and efficient BLE packet grouping algorithm; and ii) an LLM fine-tuning strategy that combines both supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). We have built a prototype of ChatTracer with four sniffing nodes. Experimental results show that ChatTracer not only outperforms existing localization approaches, but also provides an intelligent interface for user interaction.

翻译：大语言模型（LLMs）已经彻底改变了我们与网络技术的交互方式。本文探讨了将大语言模型与无线传感器网络（WSN）连接的可能性。一个成功的设计不仅能够将大语言模型的知识疆域扩展到物理世界，还将彻底革新人类与无线传感器网络的交互方式。为此，我们提出了ChatTracer，一个基于大语言模型的实时蓝牙设备追踪系统。ChatTracer包含三个关键组件：一个蓝牙嗅探节点阵列、一个数据库和一个经过微调的大语言模型。ChatTracer的设计基于我们的实验观察：商用苹果/安卓设备即使在空闲状态下，每分钟也会广播数百个BLE数据包。其创新性体现在两个方面：i）一种可靠高效的BLE数据包分组算法；以及ii）一种结合了监督微调（SFT）和基于人类反馈的强化学习（RLHF）的大语言模型微调策略。我们构建了一个包含四个嗅探节点的ChatTracer原型系统。实验结果表明，ChatTracer不仅性能优于现有的定位方法，还为用户交互提供了一个智能接口。