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Paper Abstract and Keywords
Presentation 2024-05-20 16:20
Development of an indoor location estimation method using Wi-Fi(2) -- Comparison of Application Implementation on Smart phones --
Takashi Ohkawahara, Yudai Sunagozaka, Rei Hirata, Atsuhi Koizumu, Hiroshi Oguma (NIT Toyama) SR2024-8
Abstract (in Japanese) (See Japanese page) 
(in English) We develop a machine learning model using Azure Machine Learning (Azure ML) to establish an indoor location estimation method using Wi-Fi access points and Artificial Intelligence (AI). In this study, we implement a real-time location estimation function and a mode-based location estimation function for Android smartphones. In addition, we report on the evaluation of the estimation accuracy of both functions. The real-time location estimation function produced a 2 m error accuracy of 67.3%, a 3 m error accuracy of 76.8%, and a false estimation of the floor of 2.3%. When the width of the mode was set to 16, the false estimation of the floor was reduced to 0%, the 2 m error accuracy was improved to 75.3%, and the 3 m error accuracy to 84.1%.
Keyword (in Japanese) (See Japanese page) 
(in English) Indoor Localization / Wireless LAN / Machine Learning / Received Signal Strength Indicator / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 41, SR2024-8, pp. 34-39, May 2024.
Paper # SR2024-8 
Date of Issue 2024-05-13 (SR) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SR2024-8

Conference Information
Committee SR  
Conference Date 2024-05-20 - 2024-05-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Yokacenter (Kagoshima) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Software Defined Radio, AI/ML Application, etc 
Paper Information
Registration To SR 
Conference Code 2024-05-SR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Development of an indoor location estimation method using Wi-Fi(2) 
Sub Title (in English) Comparison of Application Implementation on Smart phones 
Keyword(1) Indoor Localization  
Keyword(2) Wireless LAN  
Keyword(3) Machine Learning  
Keyword(4) Received Signal Strength Indicator  
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Keyword(6)  
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Keyword(8)  
1st Author's Name Takashi Ohkawahara  
1st Author's Affiliation NIT Toyama (NIT Toyama)
2nd Author's Name Yudai Sunagozaka  
2nd Author's Affiliation NIT Toyama (NIT Toyama)
3rd Author's Name Rei Hirata  
3rd Author's Affiliation NIT Toyama (NIT Toyama)
4th Author's Name Atsuhi Koizumu  
4th Author's Affiliation NIT Toyama (NIT Toyama)
5th Author's Name Hiroshi Oguma  
5th Author's Affiliation NIT Toyama (NIT Toyama)
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Speaker Author-1 
Date Time 2024-05-20 16:20:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # SR2024-8 
Volume (vol) vol.124 
Number (no) no.41 
Page pp.34-39 
#Pages 6 
Date of Issue 2024-05-13 (SR) 

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