Creation of Temporal Model for Prioritized Transmission in Predictive Spatial-Monitoring Using Machine Learning

• SATO Keiichiro

  Graduate School of Informatics, Kyoto University

• SHINKUMA Ryoichi

  Graduate School of Informatics, Kyoto University

• SATO Takehiro

  Graduate School of Informatics, Kyoto University

• OKI Eiji

  Graduate School of Informatics, Kyoto University

• IWAI Takanori

  System Platform Research Labs, NEC Corporation

• ONISHI Takeo

  System Platform Research Labs, NEC Corporation

• NOBUKIYO Takahiro

  System Platform Research Labs, NEC Corporation

• KANETOMO Dai

  System Platform Research Labs, NEC Corporation

• SATODA Kozo

  System Platform Research Labs, NEC Corporation

Abstract

Predictive spatial-monitoring, which predicts spatial information such as road traffic, has attracted much attention in the context of smart cities. Machine learning enables predictive spatial-monitoring by using a large amount of aggregated sensor data. Since the capacity of mobile networks is strictly limited, serious transmission delays occur when loads of communication traffic are heavy. If some of the data used for predictive spatial-monitoring do not arrive on time, prediction accuracy degrades because the prediction has to be done using only the received data, which implies that data for prediction are ‘delay-sensitive’. A utility-based allocation technique has suggested modeling of temporal characteristics of such delay-sensitive data for prioritized transmission. However, no study has addressed temporal model for prioritized transmission in predictive spatial-monitoring. Therefore, this paper proposes a scheme that enables the creation of a temporal model for predictive spatial-monitoring. The scheme is roughly composed of two steps: the first involves creating training data from original time-series data and a machine learning model that can use the data, while the second step involves modeling a temporal model using feature selection in the learning model. Feature selection enables the estimation of the importance of data in terms of how much the data contribute to prediction accuracy from the machine learning model. This paper considers road-traffic prediction as a scenario and shows that the temporal models created with the proposed scheme can handle real spatial datasets. A numerical study demonstrated how our temporal model works effectively in prioritized transmission for predictive spatial-monitoring in terms of prediction accuracy.

Journal

• IEICE Transactions on Communications

  IEICE Transactions on Communications E104.B (8), 951-960, 2021-08-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• predictive spatial-monitoring
• machine learning
• feature selection
• temporal model

Details

• CRID: 1390007437193330944
• NII Article ID: 130008070227
• DOI: 10.1587/transcom.2020ebp3175
• ISSN: 17451345
• ISSN: 09168516
• Web Site: https://www.jstage.jst.go.jp/article/transcom/E104.B/8/E104.B_2020EBP3175/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed