Indoor Localization Based on Analyzing Observed Wi-Fi Access Point Logs Using LLM

カテゴリ: 研究会(論文単位)

論文No: CMN25021

グループ名: 【C】電子・情報・システム部門 通信研究会

発行日: 2025/02/28

タイトル(英語): Indoor Localization Based on Analyzing Observed Wi-Fi Access Point Logs Using LLM

著者名: Sato Yuta(Ibaraki University),Rahmadya Budi (Universitas Andalas),Song Zequn(Ibaraki University),Hadi Danang Kumara(Ibaraki University),Takeda Shigeki(Ibaraki University),Sun Ran(Ibaraki University)

著者名(英語): Yuta Sato(Ibaraki University),Budi Rahmadya(Universitas Andalas),Zequn Song(Ibaraki University),Danang Kumara Hadi(Ibaraki University),Shigeki Takeda(Ibaraki University),Ran Sun(Ibaraki University)

キーワード: Wi-Fi|RSSI|RTLS|LLM|Indoor localization|Wi-Fi|RSSI|RTLS|LLM|Indoor localization

要約(日本語): The growing demand for indoor real-time localization systems (RTLS) has driven advancements in indoor positioning technology. Recent innovations in Wi-Fi technologies, such as Wi-Fi 6 and 7, enhance RTLS development by leveraging AI/ML methods and signal

要約(英語): The growing demand for indoor real-time localization systems (RTLS) has driven advancements in indoor positioning technology. Recent innovations in Wi-Fi technologies, such as Wi-Fi 6 and 7, enhance RTLS development by leveraging AI/ML methods and signal processing, significantly contributing to the integrated sensing and communication (ISAC) concept. The proliferation of Wi-Fi access points (APs) in workplaces, offices, and homes creates new opportunities for localization using observed Wi-Fi data. This paper proposes an indoor localization method that utilizes ChatGPT to analyze surrounding Wi-Fi APs. Experiments demonstrate that ChatGPT can accurately infer room names from unknown Wi-Fi logs containing BSSID and RSSI, based on prior data analysis.

本誌: 2025年3月3日-2025年3月4日通信研究会

本誌掲載ページ: 3-7 p

原稿種別: 英語

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