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Paper Abstract and Keywords
Presentation 2019-11-26 10:30
[Poster Presentation] Prioritized Transmission of Mobile IoT Data Using Machine Learning Models
Yuichi Inagaki, Ryoichi Shinkuma, Takehiro Sato, Oki Eiji (Kyoto Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) Predicting real-time spatial information from data collected by the mobile Internet of Things (IoT) devices is one solution to the social problems related to road traffic. The mobile IoT devices for real-time spatial information prediction generate an extremely high volume of data, making it impossible to collect all of it through mobile networks. Although some previous works have reduced the volume of transmitted data, the prediction accuracy of real-time spatial information is still not ensured. Therefore, we proposed an IoT device control system that reduces the amount of transmitted data used as input for real-time prediction while maintaining the prediction accuracy [IEEE Access, Jul. 2019]. The main contribution of the paper is that the proposed system controls data transmission from the mobile IoT devices based on the importance of data extracted from the machine learning model used for the prediction. Feature selection has been widely used for extracting the importance of data from the machine learning model. Feature selection methods were also used to reduce communication overhead in distributed learning. Unlike the conventional usage of feature selection methods, the proposed system uses them to control the data transmission of the mobile IoT devices with priority. In the paper, the proposed system is evaluated with a real-world vehicle mobility dataset in two practical scenarios using the random forest model, which is an extensively used machine learning model. The evaluation results showed that the proposed system reduces the amount of transmitted input data for real-time prediction while achieving the same level of prediction accuracy as benchmark methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Real-time spatial information / vehicular IoT / data prioritization / machine learning / feature selection / / /  
Reference Info. IEICE Tech. Rep.
Paper #  
Date of Issue  
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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Conference Information
Committee RISING  
Conference Date 2019-11-26 - 2019-11-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Fukutake Learning Theater, Hongo Campus, Univ. Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Researches on Super-Intelligent Networking, etc. 
Paper Information
Registration To RISING 
Conference Code 2019-11-RISING 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Prioritized Transmission of Mobile IoT Data Using Machine Learning Models 
Sub Title (in English)  
Keyword(1) Real-time spatial information  
Keyword(2) vehicular IoT  
Keyword(3) data prioritization  
Keyword(4) machine learning  
Keyword(5) feature selection  
1st Author's Name Yuichi Inagaki  
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 Oki Eiji  
4th Author's Affiliation Kyoto University (Kyoto Univ.)
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Date Time 2019-11-26 10:30:00 
Presentation Time 50 
Registration for RISING 
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