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

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Large language fashions (LLMs), exemplified by OpenAI ChatGPT and Google Bard, ItagPro have reworked the best way we interact with cyber applied sciences. In this paper, we research the potential for iTagPro support connecting LLM with wireless sensor networks (WSN). A profitable design won't only extend LLM’s data panorama to the bodily world but also revolutionize human interplay with WSN. To the top, we present ChatTracer, an LLM-powered real-time Bluetooth system tracking system. ChatTracer contains three key parts: an array of Bluetooth sniffing nodes, a database, and a positive-tuned LLM. ChatTracer was designed based on our experimental observation that business Apple/Android devices all the time broadcast hundreds of BLE packets per minute even in their idle status. We've got built a prototype of ChatTracer with four sniffing nodes. Experimental outcomes show that ChatTracer not only outperforms current localization approaches, but also supplies an intelligent interface for iTagPro smart device consumer interplay. The emergence of giant language models (LLM) has ushered in a transformative era, revolutionizing the best way we interact with technology and harnessing the ability of natural language processing.



To this point, to the best of our data, LLM has not yet been combined with wireless sensor networks (WSN) (Hou et al., 2023; Fan et al., 2023; Awais et al., iTagPro support 2023; Liu et al., 2023; Naveed et al., 2023; Zhao et al., 2023; Hadi et al., 2023; Guo et al., 2023; Raiaan et al., 2023; Demszky et al., 2023; Thapa and Adhikari, 2023). Connecting these two worlds is interesting for 2 causes. First, from the LLM’s perspective, connecting with WSN will extend LLM’s capabilities from generating data-based information to providing recent, real-time sensory info of our physical world. Second, from the WSN’s perspective, the use of LLM will transform the interplay between people and iTagPro product WSN, making the sensory information extra accessible and easier to grasp for end users. On this paper, ItagPro we current the first-of-its-variety study on connecting LLM with WSN, with the aim of understanding the potential of LLM in the processing of sensory data from WSN and facilitating human interaction with WSN.



Specifically, we introduce ChatTracer, an LLM-powered real-time Bluetooth machine monitoring system. ChatTracer has an array of radio sniffing nodes deployed in the world of curiosity, which keep listening to the radio indicators emitted by the Bluetooth gadgets within the proximity. ChatTracer processes its received Bluetooth packets to extract their physical and payload options utilizing domain information. The extracted per-packet options are stored in a database and fed into an LLM (Mistral-7B (Jiang et al., 2023)) to generate the human-like textual response to the queries from users. Our measurements show that, even within the powered-off status, iTagPro support the iPhone 15 Pro Max still broadcasts about 50 BLE packets per minute. We found: (i) all Android units broadcast at least a hundred and iTagPro support twenty BLE packets per minute. By decoding their BLE packets, we are able to obtain their vendor information. Compared to Android gadgets, Apple gadgets transmit BLE packets extra aggressively at a higher power. Most Apple devices transmit 300-1500 packets per minute.



Additionally, most Apple gadgets have distinctive codes (Apple continuity) in their BLE packets, making it attainable for ChatTracer to obtain their standing and activity info. These findings affirm the feasibility of using ambient Bluetooth signals for human tracking, iTagPro support and best bluetooth tracker lay the muse for ChatTracer. To design and iTagPro support implement ChatTracer, we face two challenges. The first problem lies in grouping the info packets from individual Bluetooth gadgets. ChatTracer’s radio sniffing nodes will repeatedly obtain the information packets from all Bluetooth gadgets in the world of interest. One Bluetooth machine could use different promoting addresses to send their BLE packets and randomize their advertising addresses over time (e.g., each quarter-hour). It is important for ChatTracer to group the info packets from the identical Bluetooth system. Doing so won't only enable ChatTracer to infer the total variety of Bluetooth units, but it surely may also enhance localization accuracy by growing the variety of BLE packets for machine location inference.