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

Ryoichi Shinkuma, Takayuki Nishio, Yuichi Inagaki, Eiji Oki

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number92
JournalEurasip Journal on Wireless Communications and Networking
Volume2020
Issue number1
DOIs
Publication statusPublished - 2020 Dec 1
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Signal Processing
  • Computer Science Applications
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Data assessment and prioritization in mobile networks for real-time prediction of spatial information using machine learning'. Together they form a unique fingerprint.

Cite this