Cognitive radio and machine learning modalities for enhancing the smart transportation system: A systematic literature review

• Published in: ICT express Vol. 10; no. 4; pp. 693 - 734
• Main Authors: Idris, Mohd Yamani Idna, Ahmedy, Ismail, Soon, Tey Kok, Yahuza, Muktar, Tambuwal, Abubakar Bello, Ali, Usman
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2024; Elsevier; 한국통신학회
• Subjects: Ad hoc network; Cognitive radio; Intelligent transportation system; Machine learning; Vehicular ad hoc network (VANET); 전자/정보통신공학; TV; PU; ITS; GPS; NS2; MPR; NS3; DSA; IE; Ad hoc network; IF; ML; WHO; DSRC; Vehicular ad hoc network (VANET); CRE-VANET; FCC; VANET; DOS; HSS; MEC; V2I; ES; MAC; VSM; MANET; RF; RSU; GHz; RL; CrSIMULATOR; V2V; FC; MHz; SMDP; OSLR; RQ2; RQ1; QoS; PUE; ISM; SU; LSTM; OBU; Intelligent transportation system; ACM; CCR-VANET; Cognitive radio; CR; Machine learning; TVWS; RAN
• ISSN: 2405-9595; 2405-9595
• DOI: 10.1016/j.icte.2024.05.001

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.