{"id":208994,"updated":"2025-01-19T18:39:19.861472+00:00","links":{},"created":"2025-01-19T01:10:20.181206+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00208994","sets":["581:10433:10434"]},"path":["10434"],"owner":"44499","recid":"208994","title":["An End-to-End BLE Indoor Localization Method Using LSTM"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-01-15"},"_buckets":{"deposit":"f89587e2-86de-4916-9746-8f2cb16257a2"},"_deposit":{"id":"208994","pid":{"type":"depid","value":"208994","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"An End-to-End BLE Indoor Localization Method Using LSTM","author_link":["525740","525745","525742","525739","525746","525741","525744","525743"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"An End-to-End BLE Indoor Localization Method Using LSTM"},{"subitem_title":"An End-to-End BLE Indoor Localization Method Using LSTM","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集：5G時代の社会を創るモバイル・高度交通システム（推薦論文）] location estimation, localization, BLE, deep learning, LSTM, end-to-end location estimation","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2021-01-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering, Nagoya University"},{"subitem_text_value":"Disaster Prevention Research Institute, Kyoto University"},{"subitem_text_value":"Graduate School of Engineering, Nagoya University"},{"subitem_text_value":"Graduate School of Engineering, Nagoya University／Institutes of Innovation for Future Society, Nagoya University"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Disaster Prevention Research Institute, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Nagoya University / Institutes of Innovation for Future Society, Nagoya University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/208994/files/IPSJ-JNL6201031.pdf","label":"IPSJ-JNL6201031.pdf"},"date":[{"dateType":"Available","dateValue":"2023-01-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6201031.pdf","filesize":[{"value":"5.2 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"a79be2e9-f283-4e49-827a-499e48d3a2b2","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kenta, Urano"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kei, Hiroi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuro, Yonezawa"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobuo, Kawaguchi"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kenta, Urano","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kei, Hiroi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuro, Yonezawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobuo, Kawaguchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"This paper proposes an indoor localization method for Bluetooth Low Energy (BLE) devices using an end-to-end LSTM neural network. We focus on a large-scale indoor space where there is a tough environment for wireless indoor localization due to signal instability. Our proposed method adopts end-to-end localization, which means input is a time-series of signal strength and output is the estimated location at the latest time in the input. The neural network in our proposed method consists of fully-connected and LSTM layers. We use a custom-made loss function with 3 error components: MSE, the direction of travel, and the leap of the estimated location. Considering the difficulty of data collection in a short preparation term, the data generated by a simple signal simulation is used in the training phase, before training with a small amount of real data. As a result, the estimation accuracy achieves an average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. This paper also evaluates the estimation accuracy assuming the troubles in a real operation.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.29(2021) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.29.58\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This paper proposes an indoor localization method for Bluetooth Low Energy (BLE) devices using an end-to-end LSTM neural network. We focus on a large-scale indoor space where there is a tough environment for wireless indoor localization due to signal instability. Our proposed method adopts end-to-end localization, which means input is a time-series of signal strength and output is the estimated location at the latest time in the input. The neural network in our proposed method consists of fully-connected and LSTM layers. We use a custom-made loss function with 3 error components: MSE, the direction of travel, and the leap of the estimated location. Considering the difficulty of data collection in a short preparation term, the data generated by a simple signal simulation is used in the training phase, before training with a small amount of real data. As a result, the estimation accuracy achieves an average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. This paper also evaluates the estimation accuracy assuming the troubles in a real operation.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.29(2021) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.29.58\n------------------------------","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2021-01-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"62"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}