Comprehensive survey of machine learning approaches in cognitive radio-based vehicular ad hoc networks
Document Type
Article
Publication Date
1-1-2020
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.
Keywords
Machine learning, VANET, Cognitive radio, Autonomous vehicles, Smart transportation system
Divisions
Computer
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)
Publication Title
IEEE Access
Volume
8
Publisher
Institute of Electrical and Electronics Engineers
Publisher Location
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA