ChatTracer: Large Language Model Powered Real-time Bluetooth Device Tracking System

• URL: http://arxiv.org/abs/2403.19833v2
• Date: Tue, 9 Jul 2024 12:56:01 GMT
• Title: ChatTracer: Large Language Model Powered Real-time Bluetooth Device Tracking System
• Authors: Qijun Wang, Shichen Zhang, Kunzhe Song, Huacheng Zeng,
• Abstract summary: We present ChatTracer, an LLM-powered real-time Bluetooth device tracking system. ChatTracer comprises an array of Bluetooth sniffing nodes, a database, and a fine-tuned LLM. We have built a prototype of ChatTracer with four sniffing nodes.
• Score: 7.21848268647674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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