ChatTracer: Large Language Model Powered Real-time Bluetooth Device Tracking System
<|reference_start|>ChatTracer: Large Language Model Powered Real-time Bluetooth Device Tracking System: 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.<|reference_end|>
@article{wang2024chattracer:,
    title={ChatTracer: Large Language Model Powered Real-time Bluetooth Device
  Tracking System},
    author={Qijun Wang, Shichen Zhang, Kunzhe Song, Huacheng Zeng},
    journal={arXiv preprint arXiv:2403.19833},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19833},
    primaryClass={cs.NI cs.AI}
}