Machine Learning and Internet of Things (IoT): A Bibliometric Analysis of Publications Between 2012 and 2022


Hamdan Gani(1*); Annisa Dwi Damayanti(2); Nurani Nurani(3); Sitti Zuhriyah(4); St. Nurhayati Jabir(5); Helmy Gani(6); Feng Zhipeng(7); Aisyah Sri Rejeki(8);

(1) Politeknik ATI Makassar
(2) Universitas Hasanuddin
(3) Institut Teknologi dan Bisnis Nobel Indonesia
(4) Universitas Handayani Makassar
(5) Politeknik ATI Makassar
(6) Sekolah Tinggi Ilmu Kesehatan
(7) Hangzhou Normal University
(8) Universitas Hasanuddin
(*) Corresponding Author

  

Abstract


The implementation between machine learning and the Internet of Things (IoT) has been scientifically investigated in many studies. However, not many bibliometric studies categorize the output in this area. By keeping an eye on the publications posted on the Web of Science (WoS) platform, this study aims to give a bibliometric analysis of research on Machine Learning and IoT, identifying the state of the art, trends, and other indicators. 6.170 different articles made up the sample. The VOS viewer software was used to process the data and graphically display the results. The study examined the concurrent occurrence of publications by year, keyword trends, co-citations, bibliographic coupling, and analysis of co-authorship, countries, and institutions. several prolific authors are discovered. However, the body of literature on machine learning and IoT issues is expanding quickly; only five papers accounted for more than 2193 citations. Then, 40.34 percent of the articles from the 694 sources reviewed were published as the most important paper. At the same time, the USA is the top nation for research on this subject area. In addition to identifying gaps and promising areas for future research, this study offers insight into the current state of the art and the field of machine learning and IoT.


Keywords


Bibliometric Analysis, IoT, Machine Learning, Web of Science.

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 115 times
PDF view: 33 times
     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v16i1.1700.27-37
  

Cite

References


L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, Oct. 2010, doi: 10.1016/j.comnet.2010.05.010.

M. Weiser, “The computer for the 21st Century,” IEEE Pervasive Computing, vol. 1, no. 1, pp. 19–25, 2002, doi: 10.1109/mprv.2002.993141.

A. Sheth, “Computing for human experience: Semantics-empowered sensors, services, and social computing on the ubiquitous web,” IEEE Internet Computing, vol. 14, no. 1, pp. 88–91, 2010, doi: 10.1109/MIC.2010.4.

M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, “A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security,” IEEE Communications Surveys and Tutorials, vol. 22, no. 3, pp. 1646–1685, 2020, doi: 10.1109/COMST.2020.2988293.

A. Ninkov, J. R. Frank, and L. A. Maggio, “Bibliometrics: Methods for studying academic publishing,” Perspectives on Medical Education, vol. 11, no. 3, pp. 173–176, Dec. 2021, doi: 10.1007/S40037-021-00695-4.

A. Thomas and V. Gupta, “Tacit knowledge in organizations: bibliometrics and a framework-based systematic review of antecedents, outcomes, theories, methods and future directions,” Journal of Knowledge Management, vol. 26, no. 4, pp. 1014–1041, Apr. 2022, doi: 10.1108/JKM-01-2021-0026.

J. Chen and X. Ran, “Deep Learning With Edge Computing: A Review,” Proceedings of the IEEE, vol. 107, no. 8, 2019, doi: 10.1109/JPROC.2019.2921977.

M. Roelands, “IoT service platform enhancement through ‘in-situ’ machine learning of real-world knowledge,” in 38th Annual IEEE Conference on Local Computer Networks - Workshops, Oct. 2013, pp. 896–903, doi: 10.1109/LCNW.2013.6758529.

N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, “Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset,” Future Generation Computer Systems, vol. 100, pp. 779–796, 2019, doi: 10.1016/j.future.2019.05.041.

R. Doshi, N. Apthorpe, and N. Feamster, “Machine learning DDoS detection for consumer internet of things devices,” Proceedings - 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018, no. Ml, pp. 29–35, 2018, doi: 10.1109/SPW.2018.00013.

N. Chaabouni, M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki, “Network Intrusion Detection for IoT Security Based on Learning Techniques,” IEEE Communications Surveys and Tutorials, vol. 21, no. 3, pp. 2671–2701, 2019, doi: 10.1109/COMST.2019.2896380.

L. Xiao, X. Wan, X. Lu, Y. Zhang, and D. Wu, “IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?,” IEEE Signal Processing Magazine, vol. 35, no. 5, pp. 41–49, 2018, doi: 10.1109/MSP.2018.2825478.

V. Hassija, V. Chamola, V. Saxena, D. Jain, P. Goyal, and B. Sikdar, “A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures,” IEEE Access, vol. 7, pp. 82721–82743, 2019, doi: 10.1109/ACCESS.2019.2924045.

M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, “Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,” Journal of Information Security and Applications, vol. 50, p. 102419, 2020, doi: 10.1016/j.jisa.2019.102419.

A. A. Diro and N. Chilamkurti, “Distributed attack detection scheme using deep learning approach for Internet of Things,” Future Generation Computer Systems, vol. 82, pp. 761–768, 2018, doi: 10.1016/j.future.2017.08.043.

M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for IoT big data and streaming analytics: A survey,” IEEE Communications Surveys and Tutorials, vol. 20, no. 4, pp. 2923–2960, 2018, doi: 10.1109/COMST.2018.2844341.

S. Wang et al., “When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning,” Proceedings - IEEE INFOCOM, vol. 2018-April, pp. 63–71, 2018, doi: 10.1109/INFOCOM.2018.8486403.

M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial,” IEEE Communications Surveys and Tutorials, vol. 21, no. 4, pp. 3039–3071, 2019, doi: 10.1109/COMST.2019.2926625.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Hamdan Gani, Annisa Dwi Damayanti, Nurani, Sitti Zuhriyah, St. Nurhayati Jabir, Helmy Gani, Feng Zhipeng, Aisyah Sri Rejeki.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.