| Item type |
SIG Technical Reports(1) |
| 公開日 |
2022-11-01 |
| タイトル |
|
|
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 |
|
|
言語 |
eng |
| 資源タイプ |
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|
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 |
|
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|
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) |
|
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|
en |
|
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Graduate School of Information Science and Technology, Osaka University |
| 著者所属(英) |
|
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|
en |
|
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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 |
|
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 |
|
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 |
|
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 |
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収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2188-8760 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-HCI22200016.pdf (510.4 kB)
