Data assessment and prioritization in mobile networks for real-time prediction of spatial information using machine learning

• 新熊, 亮一

  Graduate School of Informatics, Kyoto University

• 西尾, 理志

  Graduate School of Informatics, Kyoto University

• 大木, 英司

  Graduate School of Informatics, Kyoto University

• Oki, Eiji

  Graduate School of Informatics, Kyoto University

抄録

A new framework of data assessment and prioritization for real-time prediction of spatial information is presented. The real-time prediction of spatial information is promising for next-generation mobile networks. Recent developments in machine learning technology have enabled prediction of spatial information, which will be quite useful for smart mobility services including navigation, driving assistance, and self-driving. Other key enablers for forming spatial information are image sensors in mobile devices like smartphones and tablets and in vehicles such as cars and drones and real-time cognitive computing like automatic number/license plate recognition systems and object recognition systems. However, since image data collected by mobile devices and vehicles need to be delivered to the server in real time to extract input data for real-time prediction, the uplink transmission speed of mobile networks is a major impediment. This paper proposes a framework of data assessment and prioritization that reduces the uplink traffic volume while maintaining the prediction accuracy of spatial information. In our framework, machine learning is used to estimate the importance of each data element and to predict spatial information under the limitation of available data. A numerical evaluation using an actual vehicle mobility dataset demonstrated the validity of the proposed framework. Two extension schemes in our framework, which use the ensemble of importance scores obtained from multiple feature selection methods, are also presented to improve its robustness against various machine learning and feature selection methods. We discuss the performance of those schemes through numerical evaluation.

収録刊行物

• Eurasip Journal on Wireless Communications and Networking

  Eurasip Journal on Wireless Communications and Networking 2020 2020

  Springer Nature

キーワード

• Spatial information
• Real-time prediction
• Mobile crowdsensing
• Data assessment
• Machine learning
• Feature selection

詳細情報

• CRID: 1050006210047800960
• NII論文ID: 120006940299
• ISSN: 16871472
• ISSN: 16871499
• HANDLE: 2433/259779
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
  • IRDB
  • CiNii Articles
  • KAKEN