Preliminary Investigation of Distance Estimation between Smartphones via Wi-Fi Round Trip Time

• Item type: SIG Technical Reports(1)
• 公開日: 2022-11-01
• タイトル: Preliminary Investigation of Distance Estimation between Smartphones via Wi-Fi Round Trip Time
• タイトル / 言語: en
• タイトル: Preliminary Investigation of Distance Estimation between Smartphones via Wi-Fi Round Trip Time
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者名: Yuqiao, Wang; Takuya, Maekawa
• 著者名(英): Yuqiao, Wang; Takuya, Maekawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Estimating the physical distance between mobile devices such as smartphones with their Wi-Fi modules in an indoor environment has many potential real-world applications such as enhancing indoor navigation, analyzing and discovering communities, Wi-Fi geo-fencing, etc. Such distance estimation tasks have been conducted using Received Signal Strength Indication (RSSI), which leverages the strengths of signals from nearby Wi-Fi Access Points (APs). However, the imprecision of RSSI measurements has limited the performance of the RSSI-based methods. Recently, IEEE 802.11mc introduced Wi-Fi Round Trip Time (RTT) protocol, which enables distance estimation between devices and nearby APs by calculating the time-of-flight of signals, and has greatly improved the accuracy of indoor ranging. Therefore, this study presents a novel method for distance estimation between devices using Wi-Fi RTT, leveraging a graph neural network (GNN) to fully capture the geometric information among smartphones and nearby APs.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Estimating the physical distance between mobile devices such as smartphones with their Wi-Fi modules in an indoor environment has many potential real-world applications such as enhancing indoor navigation, analyzing and discovering communities, Wi-Fi geo-fencing, etc. Such distance estimation tasks have been conducted using Received Signal Strength Indication (RSSI), which leverages the strengths of signals from nearby Wi-Fi Access Points (APs). However, the imprecision of RSSI measurements has limited the performance of the RSSI-based methods. Recently, IEEE 802.11mc introduced Wi-Fi Round Trip Time (RTT) protocol, which enables distance estimation between devices and nearby APs by calculating the time-of-flight of signals, and has greatly improved the accuracy of indoor ranging. Therefore, this study presents a novel method for distance estimation between devices using Wi-Fi RTT, leveraging a graph neural network (GNN) to fully capture the geometric information among smartphones and nearby APs.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA1221543X
• 書誌情報: 研究報告ヒューマンコンピュータインタラクション（HCI）; 巻 2022-HCI-200, 号 16, p. 1-6, 発行日 2022-11-01
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8760
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会