Analysis of Machine Learning (ML) and Internet of Things (IoT): A Scientometric Review

The objective of this paper is to critically examine the publication’s trends and research landscape on the Internet of Things and Machine Learning (Io T-ML) research through bibliometric analysis (BA). The Elsevier Scopus database was selected to identify, screen, and analyze the published documents on IoT-ML. The publication trends and major stakeholders on the topic were subsequently. BA was performed to examine the co-authorships, keyword occurrences, and citations using VOSviewer software. Lastly, a systematic literature review was performed to summarize the scientific and technological advancements. Results revealed that the publications on IoT-ML largely comprise articles, conference proceedings, and reviews, whereas the publication trends showed an increase from 2 to 105 between 2015 and 2021. The top researchers on IoT-ML are S. Baskar, M. Guizani, and A. Souri, whereas the top institution is the Vellore Institute of Technology (India). However, the top country and research funder are; India, and National Natural Science Foundation (China), respectively. BA showed that the research landscape on IoT-ML comprises numerous networks of co-authors and countries working on related themes. The most impactful publication is Mahdavinejad et al. (2018) which has 479 citations, whereas the most prestigious journal is the IEEE Io T journal with 9 published documents in Scopus. Keyword analysis revealed IoT-ML has potential applications in health care, medicine, computer security and data analytics etc. Overall, IoT-ML has significant socio-economic and research impacts, and as such, the field will witness geometric techno-scientific growth and development in the future, particularly in the areas of network security and big data analytics.