{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00222062","sets":["1164:3027:10764:11024"]},"path":["11024"],"owner":"44499","recid":"222062","title":["Preliminary Investigation of Distance Estimation between Smartphones via Wi-Fi Round Trip Time"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-11-01"},"_buckets":{"deposit":"24f63b7d-143b-4e28-b151-8fb8d59f5682"},"_deposit":{"id":"222062","pid":{"type":"depid","value":"222062","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Preliminary Investigation of Distance Estimation between Smartphones via Wi-Fi Round Trip Time","author_link":["582088","582087","582090","582089"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Preliminary Investigation of Distance Estimation between Smartphones via Wi-Fi Round Trip Time"},{"subitem_title":"Preliminary Investigation of Distance Estimation between Smartphones via Wi-Fi Round Trip Time","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2022-11-01","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/222062/files/IPSJ-HCI22200016.pdf","label":"IPSJ-HCI22200016.pdf"},"date":[{"dateType":"Available","dateValue":"2024-11-01"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-HCI22200016.pdf","filesize":[{"value":"510.4 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"33"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"e5397ad0-e784-4fd7-8c4d-9c93549ec12e","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yuqiao, Wang"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yuqiao, Wang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA1221543X","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8760","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告ヒューマンコンピュータインタラクション（HCI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-11-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"16","bibliographicVolumeNumber":"2022-HCI-200"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:22:02.170688+00:00","updated":"2025-01-19T13:52:45.338039+00:00","id":222062}