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

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Large language models (LLMs), exemplified by OpenAI ChatGPT and ItagPro Google Bard, have reworked the best way we interact with cyber technologies. On this paper, we study the possibility of connecting LLM with wireless sensor iTagPro website networks (WSN). A profitable design won't solely prolong LLM’s knowledge panorama to the bodily world but additionally revolutionize human interplay with WSN. To the tip, we current ChatTracer, ItagPro an LLM-powered actual-time Bluetooth system monitoring system. ChatTracer contains three key components: an array of Bluetooth sniffing nodes, a database, and a high-quality-tuned LLM. ChatTracer was designed based mostly on our experimental observation that business Apple/Android units always broadcast hundreds of BLE packets per minute even in their idle status. Now we have constructed a prototype of ChatTracer with 4 sniffing nodes. Experimental results show that ChatTracer not solely outperforms present localization approaches, but in addition gives an clever interface for person interaction. The emergence of giant language models (LLM) has ushered in a transformative era, revolutionizing the way in which we interact with expertise and harnessing the facility of pure language processing.

To date, to the better of our information, LLM has not but been combined with wireless sensor networks (WSN) (Hou et al., 2023; Fan et al., iTagPro tracker 2023; Awais et al., 2023; Liu et al., iTagPro tracker 2023; Naveed et al., 2023; Zhao et al., 2023; Hadi et al., 2023; Guo et al., 2023; Raiaan et al., 2023; Demszky et al., iTagPro features 2023; Thapa and Adhikari, iTagPro website 2023). Connecting these two worlds is interesting for two reasons. First, from the LLM’s perspective, connecting with WSN will extend LLM’s capabilities from generating data-based information to providing contemporary, real-time sensory information of our physical world. Second, from the WSN’s perspective, iTagPro website using LLM will rework the interaction between people and WSN, making the sensory information more accessible and iTagPro website simpler to comprehend for end users. In this paper, we present the first-of-its-kind study on connecting LLM with WSN, with the goal of understanding the potential of LLM within the processing of sensory knowledge from WSN and facilitating human interaction with WSN.

Specifically, we introduce ChatTracer, an LLM-powered real-time Bluetooth device monitoring system. ChatTracer has an array of radio sniffing nodes deployed in the realm of interest, iTagPro website which keep listening to the radio signals emitted by the Bluetooth gadgets in the proximity. ChatTracer processes its acquired Bluetooth packets to extract their bodily and payload features utilizing area information. The extracted per-packet features 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, iTagPro website even in the powered-off status, the iPhone 15 Pro Max nonetheless broadcasts about 50 BLE packets per minute. We discovered: (i) all Android gadgets broadcast at the very least one hundred twenty BLE packets per minute. By decoding their BLE packets, we are able to acquire their vendor info. Compared to Android gadgets, Apple gadgets transmit BLE packets more aggressively at the next power. Most Apple gadgets transmit 300-1500 packets per minute.

Additionally, most Apple gadgets have unique codes (Apple continuity) in their BLE packets, making it potential for ChatTracer to acquire their standing and exercise info. These findings confirm the feasibility of utilizing ambient Bluetooth alerts for human tracking, and lay the foundation for ChatTracer. To design and implement ChatTracer, we face two challenges. The first challenge lies in grouping the information packets from individual Bluetooth gadgets. ChatTracer’s radio sniffing nodes will repeatedly receive the data packets from all Bluetooth devices in the world of curiosity. One Bluetooth device might use completely different promoting addresses to ship their BLE packets and randomize their promoting addresses over time (e.g., each quarter-hour). It's vital for ChatTracer to group the info packets from the same Bluetooth machine. Doing so will not solely allow ChatTracer to infer the total number of Bluetooth devices, but it will even enhance localization accuracy by rising the variety of BLE packets for gadget location inference.