Knowledge Development Trajectory of the Internet of Vehicles Domain Based on Main Path Analysis
The Internet of vehicles (IoV) is an Internet-of-things-based network in the area of transportation. It comprises sensors, network communication, automation control, and data processing and enables connectivity between vehicles and other objects. This study performed main path analysis (MPA) to inve...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/13/6120 |
| _version_ | 1827734468415193088 |
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| author | Tang-Min Hsieh Kai-Ying Chen |
| author_facet | Tang-Min Hsieh Kai-Ying Chen |
| author_sort | Tang-Min Hsieh |
| collection | DOAJ |
| description | The Internet of vehicles (IoV) is an Internet-of-things-based network in the area of transportation. It comprises sensors, network communication, automation control, and data processing and enables connectivity between vehicles and other objects. This study performed main path analysis (MPA) to investigate the trajectory of research regarding the IoV. Studies were extracted from the Web of Science database, and citation networks among these studies were generated. MPA revealed that research in this field has mainly covered media access control, vehicle-to-vehicle channels, device-to-device communications, layers, non-orthogonal multiple access, and sixth-generation communications. Cluster analysis and data mining revealed that the main research topics related to the IoV included wireless channels, communication protocols, vehicular ad hoc networks, security and privacy, resource allocation and optimization, autonomous cruise control, deep learning, and edge computing. By using data mining and statistical analysis, we identified emerging research topics related to the IoV, namely blockchains, deep learning, edge computing, cloud computing, vehicular dynamics, and fifth- and sixth-generation mobile communications. These topics are likely to help drive innovation and the further development of IoV technologies and contribute to smart transportation, smart cities, and other applications. On the basis of the present results, this paper offers several predictions regarding the future of research regarding the IoV. |
| first_indexed | 2024-03-11T01:29:30Z |
| format | Article |
| id | doaj.art-8e1da9436efd43a3a706aea0983e2bec |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:29:30Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8e1da9436efd43a3a706aea0983e2bec2023-11-18T17:30:50ZengMDPI AGSensors1424-82202023-07-012313612010.3390/s23136120Knowledge Development Trajectory of the Internet of Vehicles Domain Based on Main Path AnalysisTang-Min Hsieh0Kai-Ying Chen1College of Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, TaiwanDepartment of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, TaiwanThe Internet of vehicles (IoV) is an Internet-of-things-based network in the area of transportation. It comprises sensors, network communication, automation control, and data processing and enables connectivity between vehicles and other objects. This study performed main path analysis (MPA) to investigate the trajectory of research regarding the IoV. Studies were extracted from the Web of Science database, and citation networks among these studies were generated. MPA revealed that research in this field has mainly covered media access control, vehicle-to-vehicle channels, device-to-device communications, layers, non-orthogonal multiple access, and sixth-generation communications. Cluster analysis and data mining revealed that the main research topics related to the IoV included wireless channels, communication protocols, vehicular ad hoc networks, security and privacy, resource allocation and optimization, autonomous cruise control, deep learning, and edge computing. By using data mining and statistical analysis, we identified emerging research topics related to the IoV, namely blockchains, deep learning, edge computing, cloud computing, vehicular dynamics, and fifth- and sixth-generation mobile communications. These topics are likely to help drive innovation and the further development of IoV technologies and contribute to smart transportation, smart cities, and other applications. On the basis of the present results, this paper offers several predictions regarding the future of research regarding the IoV.https://www.mdpi.com/1424-8220/23/13/6120internet of vehiclesmain path analysiscluster analysissensor |
| spellingShingle | Tang-Min Hsieh Kai-Ying Chen Knowledge Development Trajectory of the Internet of Vehicles Domain Based on Main Path Analysis Sensors internet of vehicles main path analysis cluster analysis sensor |
| title | Knowledge Development Trajectory of the Internet of Vehicles Domain Based on Main Path Analysis |
| title_full | Knowledge Development Trajectory of the Internet of Vehicles Domain Based on Main Path Analysis |
| title_fullStr | Knowledge Development Trajectory of the Internet of Vehicles Domain Based on Main Path Analysis |
| title_full_unstemmed | Knowledge Development Trajectory of the Internet of Vehicles Domain Based on Main Path Analysis |
| title_short | Knowledge Development Trajectory of the Internet of Vehicles Domain Based on Main Path Analysis |
| title_sort | knowledge development trajectory of the internet of vehicles domain based on main path analysis |
| topic | internet of vehicles main path analysis cluster analysis sensor |
| url | https://www.mdpi.com/1424-8220/23/13/6120 |
| work_keys_str_mv | AT tangminhsieh knowledgedevelopmenttrajectoryoftheinternetofvehiclesdomainbasedonmainpathanalysis AT kaiyingchen knowledgedevelopmenttrajectoryoftheinternetofvehiclesdomainbasedonmainpathanalysis |