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Paper Abstract and Keywords
Presentation 2016-03-04 16:20
A Study on Location Estimation Method by Wi-SUN Using Machine Learning
Hiroshi Sakamoto, Hiroyuki Yasuda, Thong Huynh, Kaori Kuroda (Tokyo Univ. of Science), Yozo Shoji (NICT), Mikio Hasegawa (Tokyo Univ. of Science) CCS2015-78
Abstract (in Japanese) (See Japanese page) 
(in English) Wi-SUN is a wireless communication standard that has been developed as communication scheme for smart meter to record information of domestic consumption, such as electric energy, gas and water. In this paper, we study about the power-saving location detection system for wireless devices using the received signal strength indication (RSSI) of Wi-SUN. Since the RSSI between wireless devices is changed by the distance and the environments, in this paper, we apply machine learning algorithm for improving the accuracy of the position estimation. We use Support Vector Regression as a machine learning algorithm, and evaluate the performance of the proposed method by experiments in real environment using the Wi-SUN devices. We compare the proposed method with the conventional method, and show that it can significantly reduce the error by the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Wi-SUN / Location estimation / Received signal strength indication / Support Vector Machines / Machine learning / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 475, CCS2015-78, pp. 63-66, March 2016.
Paper # CCS2015-78 
Date of Issue 2016-02-25 (CCS) 
ISSN Print edition: ISSN 0913-5685  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. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee RCS CCS SR SRW  
Conference Date 2016-03-02 - 2016-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokyo Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To CCS 
Conference Code 2016-03-RCS-CCS-SR-SRW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Location Estimation Method by Wi-SUN Using Machine Learning 
Sub Title (in English)  
Keyword(1) Wi-SUN  
Keyword(2) Location estimation  
Keyword(3) Received signal strength indication  
Keyword(4) Support Vector Machines  
Keyword(5) Machine learning  
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Keyword(7)  
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1st Author's Name Hiroshi Sakamoto  
1st Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
2nd Author's Name Hiroyuki Yasuda  
2nd Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
3rd Author's Name Thong Huynh  
3rd Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
4th Author's Name Kaori Kuroda  
4th Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
5th Author's Name Yozo Shoji  
5th Author's Affiliation National Institute of Information and Communications Technology (NICT)
6th Author's Name Mikio Hasegawa  
6th Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
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Speaker
Date Time 2016-03-04 16:20:00 
Presentation Time 25 
Registration for CCS 
Paper # IEICE-CCS2015-78 
Volume (vol) IEICE-115 
Number (no) no.475 
Page pp.63-66 
#Pages IEICE-4 
Date of Issue IEICE-CCS-2016-02-25 


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