IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i2d10.1007_s10845-020-01590-1.html
   My bibliography  Save this article

RETRACTED ARTICLE: Visual sensor intelligent module based image transmission in industrial manufacturing for monitoring and manipulation problems

Author

Listed:
  • Bilal S. A. Alhayani

    (Yildiz Technical University)

  • Haci llhan

    (Yildiz Technical University)

Abstract

Due to technology advancement, smart visual sensing required in terms of data transfer capacity, energy-efficiency, security, and computational-efficiency. The high-quality image transmission in visual sensor networks (VSNs) consumes more space, energy, transmission delay which may experience the various security threats. Image compression is a key phase of visual sensing systems that needs to be effective. This motivates us to propose a fast and efficient intelligent image transmission module to achieve the energy-efficiency, minimum delay, and bandwidth utilization. Compressive sensing (CS) introduced to speedily compressed the image to reduces the consumption of energy, time minimization, and efficient bandwidth utilization. However, CS cannot achieve security against the different kinds of threats. Several methods introduced since the last decade to address the security challenges in the CS domain, but efficiency is a key requirement considering the intelligent manufacturing of VSNs. Furthermore, the random variables selected for the CS having the problem of recovering the image quality due to the accumulation of noise. Thus concerning the above challenges, this paper introduced a novel one-way image transmission module in multiple input multiple output that provides secure and energy-efficient with the CS model. The secured transmission in the CS domain proposed using the security matrix which is called a compressed secured matrix and perfect reconstruction with the random matrix measurement in the CS. Experimental results outwards that the intelligent module provides energy-efficient, secured transmission with low computational time as well as a reduced bit error rate.

Suggested Citation

  • Bilal S. A. Alhayani & Haci llhan, 2021. "RETRACTED ARTICLE: Visual sensor intelligent module based image transmission in industrial manufacturing for monitoring and manipulation problems," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 597-610, February.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01590-1
    DOI: 10.1007/s10845-020-01590-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01590-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-020-01590-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mingzhou Liu & Jing Ma & Ling Lin & Maogen Ge & Qiang Wang & Conghu Liu, 2017. "Intelligent assembly system for mechanical products and key technology based on internet of things," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 271-299, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    2. Yafeng Han & Tetiana Shevchenko & Bernard Yannou & Meisam Ranjbari & Zahra Shams Esfandabadi & Michael Saidani & Ghada Bouillass & Kseniia Bliumska-Danko & Guohou Li, 2023. "Exploring How Digital Technologies Enable a Circular Economy of Products," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    3. D.-Y. Kim & J.-W. Park & S. Baek & K.-B. Park & H.-R. Kim & J.-I. Park & H.-S. Kim & B.-B. Kim & H.-Y. Oh & K. Namgung & W. Baek, 2020. "A modular factory testbed for the rapid reconfiguration of manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 661-680, March.
    4. Zhi Li & Ali Vatankhah Barenji & Jiazhi Jiang & Ray Y. Zhong & Gangyan Xu, 2020. "A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 469-480, February.
    5. Maja Turk & Marko Šimic & Miha Pipan & Niko Herakovič, 2022. "Multi-Criterial Algorithm for the Efficient and Ergonomic Manual Assembly Process," IJERPH, MDPI, vol. 19(6), pages 1-17, March.
    6. Wei Fang & Lianyu Zheng, 2020. "Shop floor data-driven spatial–temporal verification for manual assembly planning," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1003-1018, April.
    7. Fernando E. Garcia-Muiña & Rocío González-Sánchez & Anna Maria Ferrari & Davide Settembre-Blundo, 2018. "The Paradigms of Industry 4.0 and Circular Economy as Enabling Drivers for the Competitiveness of Businesses and Territories: The Case of an Italian Ceramic Tiles Manufacturing Company," Social Sciences, MDPI, vol. 7(12), pages 1-31, December.
    8. Kung-Jeng Wang & Diwanda Ageng Rizqi & Hong-Phuc Nguyen, 2021. "Skill transfer support model based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1129-1146, April.
    9. Xiang T. R. Kong & Hao Luo & George Q. Huang & Xuan Yang, 2019. "Industrial wearable system: the human-centric empowering technology in Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2853-2869, December.
    10. Markus Epe & Muhammad Azmat & Dewan Md Zahurul Islam & Rameez Khalid, 2024. "Use of Smart Glasses for Boosting Warehouse Efficiency: Implications for Change Management," Logistics, MDPI, vol. 8(4), pages 1-25, October.
    11. Wai Sze Yip & Suet To & Hongting Zhou, 2022. "Current status, challenges and opportunities of sustainable ultra-precision manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2193-2205, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01590-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.