IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i5p2548-d506547.html
   My bibliography  Save this article

Revealing Development Trends in Blockchain-Based 5G Network Technologies through Patent Analysis

Author

Listed:
  • Fei Gao

    (School of Information Engineering, Putian University, Putian 351100, China)

  • De-Li Chen

    (School of Information Engineering, Putian University, Putian 351100, China)

  • Min-Hang Weng

    (School of Information Engineering, Putian University, Putian 351100, China)

  • Ru-Yuan Yang

    (Graduate Institute of Materials Engineering, National Pingtung University of Science and Technology, Pingtung County 912, Taiwan)

Abstract

The fifth-generation (5G) network has special communication and security requirements including high reliability, low latency, precise automatic control, secure covert transmission, and evidence traceability. The 5G network combined with blockchain technology just meets this demand, so it is driving a rapidly growing volume of patent applications. This study proposes application scenarios, architecture diagrams, and patent analysis methods for blockchain-based 5G network technologies, beginning with a network architecture using mobile edge computing (MEC) and blockchain as independent platform components to solve MEC load pressure. In the patent analysis, a patent cluster map of blockchain-based 5G networks is proposed to analyze the intersection of technical application fields. The bottleneck period of technological development is presented for leading countries and enterprises in the technological development of blockchain-based 5G network, highlighting relative advantages and disadvantages. Specifically, to extract the core international patent classification (IPC) key technologies and their mutual interrelatedness, we use network topology analysis to establish an IPC network topology diagram through node global and local topology characteristics, thus revealing hotspots of IPC technology research and the characteristics of the technology relationship system. The findings provide a very useful reference for the formulation of government strategy to assist in the implementation and development of blockchain-based 5G network technologies for future smart cities.

Suggested Citation

  • Fei Gao & De-Li Chen & Min-Hang Weng & Ru-Yuan Yang, 2021. "Revealing Development Trends in Blockchain-Based 5G Network Technologies through Patent Analysis," Sustainability, MDPI, vol. 13(5), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2548-:d:506547
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/5/2548/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/5/2548/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lin-Yun Huang & Jian-Feng Cai & Tien-Chen Lee & Min-Hang Weng, 2020. "A Study on the Development Trends of the Energy System with Blockchain Technology Using Patent Analysis," Sustainability, MDPI, vol. 12(5), pages 1-19, March.
    2. Sternitzke, Christian & Bartkowski, Adam & Schramm, Reinhard, 2008. "Visualizing patent statistics by means of social network analysis tools," World Patent Information, Elsevier, vol. 30(2), pages 115-131, June.
    3. Haupt, Reinhard & Kloyer, Martin & Lange, Marcus, 2007. "Patent indicators for the technology life cycle development," Research Policy, Elsevier, vol. 36(3), pages 387-398, April.
    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. Worasak Klongthong & Veera Muangsin & Chupun Gowanit & Nongnuj Muangsin, 2021. "A Patent Analysis to Identify Emergent Topics and Convergence Fields: A Case Study of Chitosan," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    2. Xiaolin Li & Hongbo Jiao & Liming Cheng & Yilin Yin & Huimin Li & Wenqing Mu & Ruirui Zhang, 2023. "A Quantitative and Qualitative Review of Blockchain Research from 2015 to 2021," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    3. JaeYeon Sim & Kyungmyung Jang, 2023. "Blockchain innovation and firm’s financial performance: patent analysis based on firm-level information," Applied Economics, Taylor & Francis Journals, vol. 55(60), pages 7178-7193, December.

    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. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    2. Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
    3. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    4. Nils Grashof, 2020. "Sinking or swimming in the cluster labour pool? A firm-specific analysis of the effect of specialized labour," Jena Economics Research Papers 2020-006, Friedrich-Schiller-University Jena.
    5. Albert, Till & Moehrle, Martin G. & Meyer, Stefan, 2015. "Technology maturity assessment based on blog analysis," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 196-209.
    6. Liu, Weiwei & Song, Yifan & Bi, Kexin, 2021. "Exploring the patent collaboration network of China's wind energy industry: A study based on patent data from CNIPA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    7. JinHyo Joseph Yun & EuiSeob Jeong & Xiaofei Zhao & Sung Deuk Hahm & KyungHun Kim, 2019. "Collective Intelligence: An Emerging World in Open Innovation," Sustainability, MDPI, vol. 11(16), pages 1-15, August.
    8. Choi, Jaewoong & Yoon, Janghyeok, 2022. "Measuring knowledge exploration distance at the patent level: Application of network embedding and citation analysis," Journal of Informetrics, Elsevier, vol. 16(2).
    9. Altuntas, Serkan & Dereli, Turkay & Kusiak, Andrew, 2015. "Analysis of patent documents with weighted association rules," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 249-262.
    10. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    11. Carolina Castaldi & Bart Los, 2008. "The identification of important innovations using tail estimators," Innovation Studies Utrecht (ISU) working paper series 08-07, Utrecht University, Department of Innovation Studies, revised Feb 2008.
    12. René Lezama-Nicolás & Marisela Rodríguez-Salvador & Rosa Río-Belver & Iñaki Bildosola, 2018. "A bibliometric method for assessing technological maturity: the case of additive manufacturing," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1425-1452, December.
    13. Cristiano Antonelli & Francesco Crespi & Christian A. Mongeau Ospina & Giuseppe Scellato, 2017. "Knowledge composition, Jacobs externalities and innovation performance in European regions," Regional Studies, Taylor & Francis Journals, vol. 51(11), pages 1708-1720, November.
    14. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    15. Jyun-Cheng Wang & Cheng-hsin Chiang & Shu-Wei Lin, 2010. "Network structure of innovation: can brokerage or closure predict patent quality?," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 735-748, September.
    16. Weiwei Liu & Yuan Tao & Zhile Yang & Kexin Bi, 2019. "Exploring and Visualizing the Patent Collaboration Network: A Case Study of Smart Grid Field in China," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
    17. Song, Kisik & Kim, Kyuwoong & Lee, Sungjoo, 2018. "Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 118-132.
    18. Jungpyo Lee & So Young Sohn, 2017. "What makes the first forward citation of a patent occur earlier?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 279-298, October.
    19. Martin Kalthaus, 2020. "Knowledge recombination along the technology life cycle," Journal of Evolutionary Economics, Springer, vol. 30(3), pages 643-704, July.
    20. Lee, Cheng-Yu & Wang, Ming-Chao & Huang, Yen-Chih, 2015. "The double-edged sword of technological diversity in R&D alliances: Network position and learning speed as moderators," European Management Journal, Elsevier, vol. 33(6), pages 450-461.

    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:gam:jsusta:v:13:y:2021:i:5:p:2548-:d:506547. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.