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Paper Abstract and Keywords
Presentation 2019-03-05 09:40
Reward-based Allocation for Mobile Crowdsensing in Real-time Prediction of Spatial Information
Rieko Takagi, Yuichi Inagaki, Ryoichi Shinkuma (Kyoto Univ.), Fatos Xhafa (UPC), Takehiro Sato, Eji Oki (Kyoto Univ.) MoNA2018-72
Abstract (in Japanese) (See Japanese page) 
(in English) Real-time prediction of spatial information has attracted a lot of attention as a potential solution to social problems such as road traffic congestion. Prediction of spatial information is performed using sensor data collected independently by geographically distributed user devices, which is called mobile crowdsensing. However, conventionally, researchers assumed that user devices provide their data altruistically and did not consider that they may refuse to do that. Likewise, in mobile crowdsensing is commonly assumed that data is truthful and of equal quality. Unfortunataley, this is not always the case of participatory systems, such as mobile crowdsensing, where users participate at will and data veracity and quality are not always ensured. Therefore, in this technical report, we propose a rewarding system that incentives user devices to provide their truthful and quality data. Our system estimates temporal and spatial importance of data provided by each user device and allocates a larger amount of reward to user devices making a larger contribution to the prediction accuracy. Performance evaluation validates the effectiveness of our system by using a real dataset.
Keyword (in Japanese) (See Japanese page) 
(in English) crowdsensing / incentive / spatial information prediction / machine learning / feature selection / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 467, MoNA2018-72, pp. 53-58, March 2019.
Paper # MoNA2018-72 
Date of Issue 2019-02-25 (MoNA) 
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)
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Conference Information
Committee ASN MoNA IPSJ-MBL IPSJ-UBI  
Conference Date 2019-03-04 - 2019-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) The University of Tokyo, Komaba Campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To MoNA 
Conference Code 2019-03-ASN-MoNA-MBL-UBI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Reward-based Allocation for Mobile Crowdsensing in Real-time Prediction of Spatial Information 
Sub Title (in English)  
Keyword(1) crowdsensing  
Keyword(2) incentive  
Keyword(3) spatial information prediction  
Keyword(4) machine learning  
Keyword(5) feature selection  
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1st Author's Name Rieko Takagi  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Yuichi Inagaki  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
3rd Author's Name Ryoichi Shinkuma  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
4th Author's Name Fatos Xhafa  
4th Author's Affiliation Polytechnic University of Catalonia (UPC)
5th Author's Name Takehiro Sato  
5th Author's Affiliation Kyoto University (Kyoto Univ.)
6th Author's Name Eji Oki  
6th Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2019-03-05 09:40:00 
Presentation Time 25 minutes 
Registration for MoNA 
Paper # MoNA2018-72 
Volume (vol) vol.118 
Number (no) no.467 
Page pp.53-58 
#Pages 6 
Date of Issue 2019-02-25 (MoNA) 

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