  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.
