ChatTracer: Large Language Model Powered Real-time Bluetooth Device Tracking System
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910519320903680 |
|---|---|
| author | Wang, Qijun Zhang, Shichen Song, Kunzhe Zeng, Huacheng |
| author_facet | Wang, Qijun Zhang, Shichen Song, Kunzhe Zeng, Huacheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19833 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ChatTracer: Large Language Model Powered Real-time Bluetooth Device Tracking System Wang, Qijun Zhang, Shichen Song, Kunzhe Zeng, Huacheng Networking and Internet Architecture Artificial Intelligence 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. |
| title | ChatTracer: Large Language Model Powered Real-time Bluetooth Device Tracking System |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2403.19833 |