Big Data in Vehicular Cloud Computing: Review, Taxonomy, and Security Challenges
| Autor: | Majed S. Alsayfi, Mohamed Y. Dahab, Fathy E. Eassa, Reda Salama, Seif Haridi, Abdullah S. Al-Ghamdi |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Elektronika ir Elektrotechnika, Vol 28, Iss 2, Pp 59-71 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1392-1215 2029-5731 |
| DOI: | 10.5755/j02.eie.30178 |
| Popis: | Modern vehicles equipped with various smart sensors have become a means of transportation and have become a means of collecting, creating, computing, processing, and transferring data while traveling through modern and rural cities. A traditional vehicular ad hoc network (VANET) cannot handle the enormous and complex data that are collected by modern vehicle sensors (e.g., cameras, lidar, and global positioning systems (GPS)) because they require rapid processing, analysis, management, storage, and uploading to trusted national authorities. Furthermore, the integrated VANET with cloud computing presents a new concept, vehicular cloud computing (VCC), which overcomes the limitations of VANET, brings new services and applications to vehicular networks, and generates a massive amount of data compared to the data collected by individual vehicles alone. Therefore, this study explored the importance of big data in VCC. First, we provide an overview of traditional vehicular networks and their limitations. Then we investigate the relationship between VCC and big data, fundamentally focusing on how VCC can generate, transmit, store, upload, and process big data to share it among vehicles on the road. Subsequently, a new taxonomy of big data in VCC was presented. Finally, the security challenges in big data-based VCCs are discussed. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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