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Paper Abstract and Keywords
Presentation 2019-01-17 10:30
Evaluation of Edge Cooperation Model for Real-Time Predictive Information Delivery Considering Prediction Accuracy
Masahiro Kanbayashi, Ryoichi Shinkuma, Takehiro Sato, Eiji Oki (Kyoto Univ.) MoNA2018-68
Abstract (in Japanese) (See Japanese page) 
(in English) Nowadays, traffic congestion is still a serious social problem in many countries. Real-time delivery of predictive road traffic information has been expected as a promising solution for the problem. Since the latency requirement is strict in such real-time delivery, edge cooperation has been studied to increase the capability of processing sensor data. However, although the prior works forced mainly on latency requirements, they did not consider the accuracy of predicted information. Therefore, this report proposes a model for edge cooperation. The edge cooperation system based on our model can maximize the prediction accuracy while satisfying the latency requirement for realtime delivery. Our model is formulated as a generalized mutual assignment problem, in which edge servers cooperatively process and use data that contribute to the improvement of prediction accuracy with high priority. This report considers a two edge-server case and discusses numerical evaluation with the scenario where roadside cameras work for collecting road-traffic information as sensors.
From the numerical result, we validate the system based on our model in terms of prediction accuracy under various conditions.
Keyword (in Japanese) (See Japanese page) 
(in English) edge computing / machine learning / predictive information delivery / generalized mutual assignment problem / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 389, MoNA2018-68, pp. 63-68, Jan. 2019.
Paper # MoNA2018-68 
Date of Issue 2019-01-09 (MoNA) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 MoNA  
Conference Date 2019-01-16 - 2019-01-17 
Place (in Japanese) (See Japanese page) 
Place (in English) T. B. D. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MoNA 
Conference Code 2019-01-MoNA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluation of Edge Cooperation Model for Real-Time Predictive Information Delivery Considering Prediction Accuracy 
Sub Title (in English)  
Keyword(1) edge computing  
Keyword(2) machine learning  
Keyword(3) predictive information delivery  
Keyword(4) generalized mutual assignment problem  
1st Author's Name Masahiro Kanbayashi  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Ryoichi Shinkuma  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
3rd Author's Name Takehiro Sato  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
4th Author's Name Eiji Oki  
4th Author's Affiliation Kyoto University (Kyoto Univ.)
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Date Time 2019-01-17 10:30:00 
Presentation Time 25 
Registration for MoNA 
Paper # IEICE-MoNA2018-68 
Volume (vol) IEICE-118 
Number (no) no.389 
Page pp.63-68 
#Pages IEICE-6 
Date of Issue IEICE-MoNA-2019-01-09 

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