Chiroma, Haruna and Abdulhamid, Shafi'i M. and Hashem, Ibrahim A. T. and Adewole, Kayode S. and Ezugwu, Absalom E. and Abubakar, Saidu and Shuib, Liyana (2021) Deep learning-based big data analytics for Internet of Vehicles: Taxonomy, challenges, and research directions. Mathematical Problems in Engineering, 2021. ISSN 1024-123X, DOI https://doi.org/10.1155/2021/9022558.
Full text not available from this repository.Abstract
The Internet of Vehicles (IoV) is a developing technology attracting attention from the industry and the academia. Hundreds of millions of vehicles are projected to be connected within the IoV environments by 2035. Each vehicle in the environment is expected to generate massive amounts of data. Currently, surveys on leveraging deep learning (DL) in the IoV within the context of big data analytics (BDA) are scarce. In this paper, we present a survey and explore the theoretical perspective of the role of DL in the IoV within the context of BDA. The study has unveiled substantial research opportunities that cut across DL, IoV, and BDA. Exploring DL in the IoV within BDA is an infant research area requiring active attention from researchers to fully understand the emerging concept..e survey proposes a model of IoV environment integrated into the cloud equipped with a high-performance computing server, DL architecture, and Apache Spark for data analytics. The current developments, challenges, and opportunities for future research are presented. This study can guide expert and novice researchers on further development of the application of DL in the IoV within the context of BDA.
| Item Type: | Article |
|---|---|
| Funders: | Federal Ministry of Education, Federal Government of Nigeria, Tertiary Education Trust Fund (TETFund), Institutional Based Research (IBR) Fund, through Federal College of Education (Technical), Gombe[TETFund/R&D/FCETGombe/IBR/0001] |
| Uncontrolled Keywords: | Restricted boltzmann machine;Belief networks;Connected vehicles;System;Text |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 14 Oct 2022 06:19 |
| Last Modified: | 14 Oct 2022 06:19 |
| URI: | http://eprints.um.edu.my/id/eprint/35312 |
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