Cognitive radio and machine learning modalities for enhancing the smart transportation system: A systematic literature review
Document Type
Article
Publication Date
8-1-2024
Abstract
Smart transportation systems implemented through vehicular ad hoc networks (VANET) offer significant potential to improve safety. However, the network faces critical challenges related to security, as well as inadequate spectrum sensing and management. To address these issues, researchers have utilized cognitive radio and machine learning technologies. Although, previous survey studies have provided a valuable foundation for understanding the use of cognitive radio in VANET, not all have systematically investigated its impact on mitigating spectrum sensing and management issues or the role of machine learning in supporting cognitive radio functionality. Furthermore, the effects of security issues on both VANET and cognitive radio enhanced VANET have not been consistently examined. This survey aims to systematically review the application of cognitive radio and machine learning approaches to address the identified challenges in smart transportation networks, offering valuable research opportunities for future investigations. The paper extensively explores state-of-the-art approaches and focuses on: (1) Assessing the impact of cognitive radio and machine learning on spectrum sensing and management in smart transportation networks and (2) Evaluating the impact of security issues on both VANET and cognitive radio enhanced VANET. (c) 2024 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords
Ad hoc network, Cognitive radio, Machine learning, Intelligent transportation system, Vehicular ad hoc network (VANET)
Divisions
fsktm
Funders
Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme (FRGS/1/2021/ICT11/UM/02/1 (FP005-2021))
Publication Title
ICT Express
Volume
10
Issue
4
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS