4.6 Article

Scientometric analysis of literature on distributed vehicular networks : VOSViewer visualization techniques

期刊

ARTIFICIAL INTELLIGENCE REVIEW
卷 54, 期 8, 页码 6309-6341

出版社

SPRINGER
DOI: 10.1007/s10462-021-09980-4

关键词

VANET; Bibliometrics; Scientometrics; Routing protocols; IOT; Big data; Cloud computing

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The concept of vehicular communication has evolved significantly over the past two decades, progressing from simple signaling lights to high-performance and low-latency technologies. The introduction of technologies such as VANET and V2X in recent years has sparked widespread interest in research on connected vehicles.
The idea of vehicular communication has evolved dramatically over the past two decades apparently with technological advancements that accord low power and memory consumption devices, and yet providing low latency and high performing abilities. In the 20th century, vehicular communications were primarily done through singling blinkers, tail lights, and hazard lamps, essentially serving rudimentary purposes. In the early 2000s, the term VANET was first introduced for demonstrating road safety and navigation. By the end of 2012, the term vehicle-to-everything(V2X) was coined with IEEE 802.11p as the supporting standard for underneath technology. The adequately increasing publications and development of VANET simulators like SUMO acknowledge the fact that there has been an intriguing interest in vehicular research. While publications are increasing massively in numbers, perhaps, we identified a lack of comprehensive scientometric analysis for this field. This paper aims at analyzing the researches carried out between 2009 and 2020 using Scopus data with a scientometric approach thereby highlighting the citation patterns and indulging journals, authors, institutes, countries in the field of vehicular communication. Furthermore, keywords analysis is also staged using VOSViewer to visualize the research patterns. This paper also tries to classify the publications into distinct clusters so formed in VOSViewer using classifying techniques, hence, to make the voluminous data acumen for the publication stride.

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