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Weighted Robust Tensor Principal Component Analysis for the Recovery of Complex Corrupted Data in a 5G-Enabled Internet of Things †

Applied Sciences (Switzerland), ISSN: 2076-3417, Vol: 14, Issue: 10
2024
  • 1
    Citations
  • 0
    Usage
  • 3
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1
  • Captures
    3
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • 1

Most Recent News

Research from Soongsil University in the Area of Applied Sciences Described (Weighted Robust Tensor Principal Component Analysis for the Recovery of Complex Corrupted Data in a 5G-Enabled Internet of Things)

2024 JUN 04 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Science Daily -- Current study results on applied sciences have been published.

Article Description

Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of Things (IoTs) crucial for measurement and sensing. Although 5G-enabled IoTs are vital, they face challenges in data integrity, such as mixed noise, outliers, and missing values, owing to various transmission issues. Traditional methods such as the tensor robust principal component analysis (TRPCA) have limitations in preserving essential data. This study introduces an enhanced approach, the weighted robust tensor principal component analysis (WRTPCA), combined with weighted tensor completion (WTC). The new method enhances data recovery using tensor singular value decomposition (t-SVD) to separate regular and abnormal data, preserve significant components, and robustly address complex data corruption issues, such as mixed noise, outliers, and missing data, with the globally optimal solution determined through the alternating direction method of multipliers (ADMM). Our study is the first to address complex corruption in multivariate data using the WTRPCA. The proposed approach outperforms current techniques. In all corrupted scenarios, the normalized mean absolute error (NMAE) of the proposed method is typically less than 0.2, demonstrating strong performance even in the most challenging conditions in which other models struggle. This highlights the effectiveness of the proposed approach in real-world 5G-enabled IoTs.

Bibliographic Details

Hanh Hong Phuc Vo; Thuan Minh Nguyen; Myungsik Yoo

MDPI AG

Materials Science; Physics and Astronomy; Engineering; Chemical Engineering; Computer Science

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