Cognitive radio and machine learning modalities for enhancing the smart transportation system: A systematic literature review
Mohd Yamani Idna Idris,
Ismail Ahmedy,
Tey Kok Soon,
Muktar Yahuza,
Abubakar Bello Tambuwal,
Usman Ali
Affiliations
Mohd Yamani Idna Idris
Faculty of Computer Science and Information Technology, Department of Computer System and Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia; Center for Mobile Cloud Computing, Faculty of Computer Science and Information Technology, University Malaya, Kuala Lumpur, 50603, Malaysia; Corresponding author at: Faculty of Computer Science and Information Technology, Department of Computer System and Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Ismail Ahmedy
Faculty of Computer Science and Information Technology, Department of Computer System and Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
Tey Kok Soon
Faculty of Computer Science and Information Technology, Department of Computer System and Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
Muktar Yahuza
Faculty of Computer Science and Information Technology, Department of Computer System and Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia; Department of Computer Science, Yobe State University Damaturu, 620242, Nigeria; Corresponding author at: Department of Computer Science, Yobe State University Damaturu, 620242, Nigeria.
Abubakar Bello Tambuwal
Department of Computer Science, Umaru Ali Shinkafi Polytechnic, Sokoto State, Nigeria
Usman Ali
Department of Computer and Software Technology, University of Swat, Saidu Sharif 19130, Pakistan
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.
