Data Importance Aware Periodic Machine Learning Model Update for Sparse Mobile Crowdsensing

Yuichi Inagaki, Ryoichi Shinkuma, Takehiro Sato, Eiji Oki

研究成果: Conference article査読

抄録

Sparse mobile crowdsensing is a crowdsensing paradigm that reduces the sensing cost while ensuring data quality by collecting data sparsely and reconstructing desired data using inference algorithms including machine learning algorithms. However, real-time inference of spatial information with sparse mobile crowdsensing has not sufficiently considered the change of temporal characteristics of data. As a result, the accuracy of the reconstructed data can deteriorate over time. Therefore, this paper proposes a framework that periodically updates a machine learning model used for reconstructing data by evaluating the importance of the data in terms of both inference and re-training and giving priority to collecting important data.

本文言語English
ページ(範囲)667-670
ページ数4
ジャーナルProceedings - IEEE Consumer Communications and Networking Conference, CCNC
DOI
出版ステータスPublished - 2022
イベント19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 - Virtual, Online, United States
継続期間: 2022 1月 82022 1月 11

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学

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