Hossain, Mohammad Asif and Md. Noor, Rafidah and Yau, Kok-Lim Alvin and Azzuhri, Saaidal Razalli and Z'aba, Muhammad Reza and Ahmedy, Ismail (2020) Comprehensive survey of machine learning approaches in cognitive radio-based vehicular ad hoc networks. IEEE Access, 8. pp. 78054-78108. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2020.2989870.
Full text not available from this repository.Abstract
Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and low-latency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driver-less vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed.
| Item Type: | Article |
|---|---|
| Funders: | Sunway University (CR-UM-SST-DCIS-2018-01), Sunway University (RK004-2017), Universiti Malaya (CR-UM-SST-DCIS-2018-01), Universiti Malaya (RK004-2017) |
| Uncontrolled Keywords: | Machine learning; VANET; Cognitive radio; Autonomous vehicles; Smart transportation system |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Computer System & Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 14 Jun 2023 00:46 |
| Last Modified: | 14 Jun 2023 00:46 |
| URI: | http://eprints.um.edu.my/id/eprint/37031 |
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