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Paper Abstract and Keywords
Presentation 2024-10-10 16:05
Indoor Localization Using Router-to-Router RSSI and Transfer Learning for Dynamic Environments
Liuyi Yang, Patrick Finnerty, Chikara Ohta (Kobe Univ.) NS2024-109
Abstract (in Japanese) (See Japanese page) 
(in English) With the increasing demand for indoor localization, received signal strength indicator (RSSI)-based fingerprint localization has gained widespread attention due to its low equipment costs. Traditional methods use RSSI data collected from user devices as fingerprint features to train localization models. We extend this method by also collecting RSSI data between wireless access routers as additional fingerprint features, resulting in a nearly 20% increase in localization accuracy. However, changes in the indoor environment, such as opening and closing doors or moving furniture, could render existing models ineffective. Re-collecting data and re-training models would be labor-intensive and time-consuming. To address this issue, we propose a multi-task domain-adversarial transfer learning method that extracts consistent features before and after environment changes. By re-collecting only 20% of the data, our method improves localization accuracy from 19% to 41% and reduces the mean error distance (MED) from 3 m to 1.7 m.
Keyword (in Japanese) (See Japanese page) 
(in English) RSSI / transfer learning / indoor localization / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 197, NS2024-109, pp. 103-108, Oct. 2024.
Paper # NS2024-109 
Date of Issue 2024-10-02 (NS) 
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 NS2024-109

Conference Information
Committee NS  
Conference Date 2024-10-09 - 2024-10-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokushima University + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network architecture (5G, Local 5G, Beyond5G, Mobile networks, Ad-hoc and sensor networks, Overlay and P2P networks, Programmable networks, SDN/NFV, IoT, Network slicing), Next generation packet transport (High speed Ethernet, IP over WDM, Multi-service package technology, MPLS), Grid, etc. 
Paper Information
Registration To NS 
Conference Code 2024-10-NS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Indoor Localization Using Router-to-Router RSSI and Transfer Learning for Dynamic Environments 
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Keyword(1) RSSI  
Keyword(2) transfer learning  
Keyword(3) indoor localization  
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1st Author's Name Liuyi Yang  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Patrick Finnerty  
2nd Author's Affiliation Kobe University (Kobe Univ.)
3rd Author's Name Chikara Ohta  
3rd Author's Affiliation Kobe University (Kobe Univ.)
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Speaker Author-1 
Date Time 2024-10-10 16:05:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2024-109 
Volume (vol) vol.124 
Number (no) no.197 
Page pp.103-108 
#Pages 6 
Date of Issue 2024-10-02 (NS) 

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