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Erschienen in: Artificial Intelligence Review 8/2021

08.03.2021

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

verfasst von: Sandeep Kumar Sood, Navin Kumar, Munish Saini

Erschienen in: Artificial Intelligence Review | Ausgabe 8/2021

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Abstract

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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Metadaten
Titel
Scientometric analysis of literature on distributed vehicular networks : VOSViewer visualization techniques
verfasst von
Sandeep Kumar Sood
Navin Kumar
Munish Saini
Publikationsdatum
08.03.2021
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 8/2021
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-021-09980-4

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