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Paper Abstract and Keywords
Presentation 2024-07-17 10:15
[Short Paper] A Study on Bayesian Optimization-Based Dynamic Sensor Selection for Efficient Spatio-Temporal Monitoring Systems
Taiyo Nakamura, Koya Sato (UEC) SR2024-23
Abstract (in Japanese) (See Japanese page) 
(in English) Demand for sensing-based spatio-temporal forecasting is increasing. When forecasting large-scale spatio-temporal data, although increasing the number of sensors improves the forecasting accuracy, it increases costs (e.g., power consumption and communication). In this paper, we propose a dynamic sensor selection method based on adaptive experimental design with Bayesian optimization, which aims at ensuring observation accuracy and reducing the overall system cost. Our method selects the active sensors based on Bayesian optimization so that the difference between the actual value and the value predicted using only active sensors is slight .We also introduce a resampling mechanism that focuses on the variation in the statistical properties of the spatio-temporal data. Numerical results from a meteorological dataset demonstrate that our method can perform spatiotemporal forecasting with a limited number of sensors without compromising accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Spatio-temporal prediction / Gaussian process / Bayesian optimization / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 108, SR2024-23, pp. 4-6, July 2024.
Paper # SR2024-23 
Date of Issue 2024-07-10 (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 SR2024-23

Conference Information
Committee RCC RCS SeMI NS SR RISING  
Conference Date 2024-07-17 - 2024-07-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Citizens Activities Promotion Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SR 
Conference Code 2024-07-RCC-RCS-SeMI-NS-SR-RISING 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Bayesian Optimization-Based Dynamic Sensor Selection for Efficient Spatio-Temporal Monitoring Systems 
Sub Title (in English)  
Keyword(1) Spatio-temporal prediction  
Keyword(2) Gaussian process  
Keyword(3) Bayesian optimization  
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1st Author's Name Taiyo Nakamura  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Koya Sato  
2nd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2024-07-17 10:15:00 
Presentation Time 15 minutes 
Registration for SR 
Paper # SR2024-23 
Volume (vol) vol.124 
Number (no) no.108 
Page pp.4-6 
#Pages 3 
Date of Issue 2024-07-10 (SR) 

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