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Title: Prioritization of Mobile IoT Data Transmission Based on Data Importance Extracted from Machine Learning Model
Authors: Inagaki, Yuichi
Shinkuma, Ryoichi  kyouindb  KAKEN_id
Sato, Takehiro  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-6253-0942 (unconfirmed)
Oki, Eiji  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0003-2177-5027 (unconfirmed)
Author's alias: 稲垣, 悠一
新熊, 亮一
佐藤, 丈博
大木, 英司
Keywords: Real-time spatial information
vehicular IoT
data prioritization
machine learning
feature selection
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Journal title: IEEE Access
Volume: 7
Start page: 93611
End page: 93620
Abstract: 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, this paper proposes an IoT device control system that reduces the amount of transmitted data used as input for real-time prediction while maintaining the prediction accuracy. The main contribution of this 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 this 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 show 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.
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
URI: http://hdl.handle.net/2433/246203
DOI(Published Version): 10.1109/ACCESS.2019.2928216
Appears in Collections:Journal Articles

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