Skip to main content
SpringerLink
Log in

A Bibliometric Analysis of Convergence of Artificial Intelligence and Blockchain for Edge of Things

  • Research
  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

The convergence of Artificial Intelligence (AI) and Blockchain technologies has emerged as a powerful paradigm to address the challenges of data management, security, and privacy in the Edge of Things (EoTs) environment. This bibliometric analysis aims to explore the research landscape and trends surrounding the topic of convergence of AI and Blockchain for EoTs to gain insights into its development and potential implications. For this, research published during the past six years (2018-2023) in the Web of Science indexed sources has been considered as it has been a new field. VoSViewer-based full counting methodology has been used to analyze citation, co-citation, and co-authorship based collaborations among authors, organizations, countries, sources, and documents. The full counting method in VoSViewer involves considering all authors or sources with equal weight when calculating various bibliometric indicators. Co-occurrence, timeline, and burst detection analysis of keywords and published articles were also carried out to unravel significant research trends on the convergence of AI and Blockchain for EoTs. Our findings reveal a steady growth in research output, indicating the increasing importance and interest in AI-enabled Blockchain solutions for EoTs. Further, the analysis uncovered key influential researchers and institutions driving advancements in this domain, shedding light on potential collaborative networks and knowledge hubs. Additionally, the study examines the evolution of research themes over time, offering insights into emerging areas and future research directions. This bibliometric analysis contributes to the understanding of the state-of-the-art in convergence of AI and Blockchain for EoTs, highlighting the most influential works and identifying knowledge gaps. Researchers, industry practitioners, and policymakers can leverage these findings to inform their research strategies and decision-making processes, fostering innovation and advancements in this cutting-edge interdisciplinary field.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Sekhar, R., Sharma, D., Shah, P.: State of the art in metal matrix composites research: A bibliometric analysis. Appl. Sys. Innov. 4(4), 86 (2021)

    Article  Google Scholar 

  2. Jiang, Y., Ritchie, B.W., Benckendorff, P.: Bibliometric visualisation: An application in tourism crisis and disaster management research. Current Issues in Tourism 22(16), 1925–1957 (2019)

    Article  Google Scholar 

  3. Sharma, D., Gupta, P.K., Andreu-Perez, J.: A review on cyber physical systems and smart computing: Bibliometric analysis. Metaheuristic Algorithms in Industry 4, 1–31 (2021)

    Google Scholar 

  4. Raan, A.F.: For your citations only? hot topics in bibliometric analysis. Meas. Interdisc. Res. Perspect. 3(1), 50–62 (2005)

    Article  Google Scholar 

  5. Ye, Q., Song, H., Li, T.: Cross-institutional collaboration networks in tourism and hospitality research. Tour. Manag. Perspect. 2, 55–64 (2012)

    Google Scholar 

  6. Zupic, I., Čater, T.: Bibliometric methods in management and organization. Organ. Res. Methods. 18(3), 429–472 (2015)

    Article  Google Scholar 

  7. Borgman, C.L., Furner, J.: Scholarly communication and bibliometrics. Ann. Rev. Inf. Sci. Technol 36(1), 1–53 (2002)

    Google Scholar 

  8. McKercher, B., Law, R., Lam, T.: Rating tourism and hospitality journals. Tour. Manag. 27(6), 1235–1252 (2006)

    Article  Google Scholar 

  9. Cheng, C.-K., Li, X.R., Petrick, J.F., O’Leary, J.T.: An examination of tourism journal development. Tour. Manag. 32(1), 53–61 (2011)

    Article  Google Scholar 

  10. Baggio, R., Scott, N., Arcodia, C.: Collaboration in the events literature: a co-authorship network study. Proceedings of the EUTO, 1–16 (2008)

  11. Hu, C., Racherla, P.: Visual representation of knowledge networks: A social network analysis of hospitality research domain. Int. J. Hosp. Manag. 27(2), 302–312 (2008)

    Article  Google Scholar 

  12. White, H.D., McCain, K.W.: Visualizing a discipline: An author co-citation analysis of information science, 1972–1995. J. Am. Soc. Inf. Sci. 49(4), 327–355 (1998)

    Google Scholar 

  13. Benckendorff, P., Zehrer, A.: A network analysis of tourism research. Ann. Tour. Res. 43, 121–149 (2013)

    Article  Google Scholar 

  14. Jamal, T., Smith, B., Watson, E.: Ranking, rating and scoring of tourism journals: Interdisciplinary challenges and innovations. Tour. Manag. 29(1), 66–78 (2008)

    Article  Google Scholar 

  15. Benckendorff, P.: Themes and trends in australian and new zealand tourism research: A social network analysis of citations in two leading journals (1994–2007). J. Hosp. Tour. Manag. 16(1), 1–15 (2009)

    Article  Google Scholar 

  16. McKercher, B.: A citation analysis of tourism scholars. Tour. Manag. 29(6), 1226–1232 (2008)

    Article  Google Scholar 

  17. Cobo, M.J., López-Herrera, A.G., Herrera-Viedma, E., Herrera, F.: Science mapping software tools: Review, analysis, and cooperative study among tools. J. Am. Soc. Inf. Sci. Technol. 62(7), 1382–1402 (2011)

    Article  Google Scholar 

  18. Fuller, A., Fan, Z., Day, C., Barlow, C.: Digital twin: Enabling technologies, challenges and open research. IEEE access 8, 108952–108971 (2020)

    Article  Google Scholar 

  19. Klerkx, L., Jakku, E., Labarthe, P.: A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS-Wageningen J. Life Sci. 90, 100315 (2019)

  20. Maddikunta, P.K.R., Pham, Q.-V., Prabadevi, B., Deepa, N., Dev, K., Gadekallu, T.R., Ruby, R., Liyanage, M.: Industry 5.0: A survey on enabling technologies and potential applications. J. Ind. Inf. Integr 26, 100257 (2022)

  21. Lezoche, M., Hernandez, J.E., Díaz, M.d.M.E.A., Panetto, H., Kacprzyk, J.: Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Comput. Ind. 117, 103187 (2020)

  22. Liu, Y., Ma, X., Shu, L., Hancke, G.P., Abu-Mahfouz, A.M.: From industry 4.0 to agriculture 4.0: Current status, enabling technologies, and research challenges. IEEE Trans. Ind. Informa. 17(6), 4322–4334 (2020)

  23. Allam, Z., Dhunny, Z.A.: On big data, artificial intelligence and smart cities. Cities 89, 80–91 (2019)

    Article  Google Scholar 

  24. Singh, S., Sharma, P.K., Yoon, B., Shojafar, M., Cho, G.H., Ra, I.-H.: Convergence of blockchain and artificial intelligence in iot network for the sustainable smart city. Sustain. Cities Soc 63, 102364 (2020)

    Article  Google Scholar 

  25. Qadri, Y.A., Nauman, A., Zikria, Y.B., Vasilakos, A.V., Kim, S.W.: The future of healthcare internet of things: a survey of emerging technologies. IEEE Commun. Surv. Tutorials 22(2), 1121–1167 (2020)

    Article  Google Scholar 

  26. Singh, S.K., Rathore, S., Park, J.H.: Blockiotintelligence: A blockchain-enabled intelligent iot architecture with artificial intelligence. Futur. Gener. Comput. Syst. 110, 721–743 (2020)

    Article  Google Scholar 

  27. Khan, W.Z., Rehman, M., Zangoti, H.M., Afzal, M.K., Armi, N., Salah, K.: Industrial internet of things: Recent advances, enabling technologies and open challenges. Comp. Electr. Eng. 81, 106522 (2020)

    Article  Google Scholar 

  28. Khan, M.A., Salah, K.: Iot security: Review, blockchain solutions, and open challenges. Futur. Gener. Comput. Syst. 82, 395–411 (2018)

    Article  Google Scholar 

  29. Atzori, L., Iera, A., Morabito, G.: The internet of things: A survey. Comput. Netw. 54(15), 2787–2805 (2010)

    Article  Google Scholar 

  30. Reyna, A., Martín, C., Chen, J., Soler, E., Díaz, M.: On blockchain and its integration with iot. challenges and opportunities. Futur. Gener. Comput. Syst. 88, 173–190 (2018)

  31. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutorials 17(4), 2347–2376 (2015)

    Article  Google Scholar 

  32. Fernández-Caramés, T.M., Fraga-Lamas, P.: A review on the use of blockchain for the internet of things. Ieee Access 6, 32979–33001 (2018)

    Article  Google Scholar 

  33. Christidis, K., Devetsikiotis, M.: Blockchains and smart contracts for the internet of things. Ieee Access 4, 2292–2303 (2016)

    Article  Google Scholar 

  34. Yang, R., Yu, F.R., Si, P., Yang, Z., Zhang, Y.: Integrated blockchain and edge computing systems: A survey, some research issues and challenges. IEEE Commun. Surv. Tutorials 21(2), 1508–1532 (2019)

    Article  Google Scholar 

  35. Ali, M.S., Vecchio, M., Pincheira, M., Dolui, K., Antonelli, F., Rehmani, M.H.: Applications of blockchains in the internet of things: A comprehensive survey. IEEE Commun. Surv. Tutorials 21(2), 1676–1717 (2018)

    Article  Google Scholar 

  36. Dai, H.-N., Zheng, Z., Zhang, Y.: Blockchain for internet of things: A survey. IEEE Internet Things J. 6(5), 8076–8094 (2019)

    Article  Google Scholar 

  37. Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., Liu, Y.: Privacy-preserving blockchain-based federated learning for iot devices. IEEE Internet Things J. 8(3), 1817–1829 (2020)

    Article  Google Scholar 

  38. Castro, M., Liskov, B.: Practical byzantine fault tolerance and proactive recovery. ACM Trans. Comput. Syst. (TOCS) 20(4), 398–461 (2002)

    Article  Google Scholar 

  39. Kang, J., Xiong, Z., Niyato, D., Xie, S., Zhang, J.: Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory. IEEE Internet Things J. 6(6), 10700–10714 (2019)

    Article  Google Scholar 

  40. Mohammadi, M., Al-Fuqaha, A., Sorour, S., Guizani, M.: Deep learning for iot big data and streaming analytics: A survey. IEEE Commun. Surv. Tutorials 20(4), 2923–2960 (2018)

    Article  Google Scholar 

  41. Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., McCallum, P., Peacock, A.: Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renewable and sustainable energy reviews 100, 143–174 (2019)

    Article  Google Scholar 

  42. Zheng, Z., Xie, S., Dai, H., Chen, X., Wang, H.: An overview of blockchain technology: Architecture, consensus, and future trends. In: 2017 IEEE International Congress on Big Data (BigData Congress), pp. 557–564 (2017). Ieee

  43. Salah, K., Rehman, M.H.U., Nizamuddin, N., Al-Fuqaha, A.: Blockchain for ai: Review and open research challenges. IEEE Access 7, 10127–10149 (2019)

    Article  Google Scholar 

  44. Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.-J.: Big data in smart farming-a review. Agric. Syst. 153, 69–80 (2017)

    Article  Google Scholar 

  45. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Trans. Intell. Sys. Technol. (TIST) 10(2), 1–19 (2019)

    Article  Google Scholar 

  46. Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Blockchain and federated learning for privacy-preserved data sharing in industrial iot. IEEE Trans. Ind. Inf. 16(6), 4177–4186 (2019)

    Article  Google Scholar 

  47. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: Vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)

  48. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (iot): A vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29(7), 1645–1660 (2013)

    Article  Google Scholar 

  49. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W.: A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017)

    Article  Google Scholar 

  50. Wang, F., Cui, J., Zhang, Q., He, D., Gu, C., Zhong, H.: Blockchain-based lightweight message authentication for edge-assisted cross-domain industrial internet of things. IEEE Trans, Dependable Secure Comput (2023)

    Book  Google Scholar 

  51. Li, W., Zhang, Q., Deng, S., Zhou, B., Wang, B., Cao, J.: Q-learning improved lightweight consensus algorithm for blockchain-structured internet of things. IEEE Internet Things J. (2023)

  52. Jin, C., Bao, Z., Miao, W., Zeng, Z., Wei, X., Zhang, R.: A lightweight nonlinear white-box sm4 implementation applied to edge iot agents. IEEE Access (2023)

Download references

Funding

This research was supported by Basic Science Research Program through the Na-tional Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A3049788) and Brain Pool program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (2019H1D3A1A01101687, 2021H1D3A2A01099390).

Author information

Authors and Affiliations

  1. Department of Computer Science, Christian-Albrechts-Universität zu Kiel, Kiel, 24118, Schleswig-Holstein, Germany

    Deepak Sharma

  2. Blockchain Technology Research Lab, Department of Computer Science and Engineering, Delhi Technological University, Delhi, 110042, New Delhi, India

    Rajeev Kumar

  3. Department of Software Convergence, Andong National University, Andong, Gyeongbuk, 36729, Gyeongsangbuk-do, Republic of Korea

    Ki-Hyun Jung

Authors
  1. Deepak Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajeev Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ki-Hyun Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.K. wrote the main manuscript text and D.K. prepared figures and tables. K.-H.J. reviewed the manuscript.

Corresponding author

Correspondence to Rajeev Kumar.

Ethics declarations

Conflicts of interest

The authors declare no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, D., Kumar, R. & Jung, KH. A Bibliometric Analysis of Convergence of Artificial Intelligence and Blockchain for Edge of Things. J Grid Computing 21, 79 (2023). https://doi.org/10.1007/s10723-023-09716-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10723-023-09716-4

Keywords

Search

Navigation