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Edge Computing Data Optimization for Smart Quality Management: Industry 5.0 Perspective

Sustainability (Switzerland), ISSN: 2071-1050, Vol: 15, Issue: 7
2023
  • 24
    Citations
  • 0
    Usage
  • 109
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    24
  • Captures
    109
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • News
        1

Most Recent News

Study Findings from University of Novi Sad Broaden Understanding of Information and Data Optimization (Edge Computing Data Optimization for Smart Quality Management: Industry 5.0 Perspective)

2023 APR 13 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- Current study results on information and data optimization have

Article Description

In the last decade, researchers have focused on digital technologies within Industry 4.0. However, it seems the Industry 4.0 hype did not fulfil industry expectations due to many implementation challenges. Today, Industry 5.0 proposes a human-centric approach to implement digital sustainable technologies for smart quality improvement. One important aspect of digital sustainability is reducing the energy consumption of digital technologies. This can be achieved through a variety of means, such as optimizing energy efficiency, and data centres power consumption. Complementing and extending features of Industry 4.0, this research develops a conceptual model to promote Industry 5.0. The aim of the model is to optimize data without losing significant information contained in big data. The model is empowered by edge computing, as the Industry 5.0 enabler, which provides timely, meaningful insights into the system, and the achievement of real-time decision-making. In this way, we aim to optimize data storage and create conditions for further power and processing resource rationalization. Additionally, the proposed model contributes to Industry 5.0 from a social aspect by considering the knowledge, not only of experienced engineers, but also of workers who work on machines. Finally, the industrial application was done through a proof-of-concept using manufacturing data from the process industry, where the amount of data was reduced by 99.73% without losing significant information contained in big data.

Bibliographic Details

Bojana Bajic; Slobodan Moraca; Aleksandar Rikalovic; Milos Jovicic; Nikola Suzic; Miladin Stefanović

MDPI AG

Computer Science; Social Sciences; Energy; Engineering; Environmental Science

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