Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

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 lowlatency 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 driverless vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed. INDEX TERMS Machine learning, VANET, cognitive radio, autonomous vehicles, smart transportation system. performance of CR-VANET [15]. Security enhancement is one of the major issues in CR-VANET. Here, a vehicle can pretend to be a PU and propagate false information to obtain spectrum access selfishly. ML can be used to detect such actions and enhance security [16], [17]. ML also provides an optimum route to CR-VANET users to avoid traffic jams and road accidents. ML can also play a vital role in the best infotainment experience in CR-VANET. It can be used for appropriate scheduling, selecting the best channel, and prioritizing messages. CR and ML can play a major role in the next-generation driverless car system. The role of CR in the next-generation transportation system has been presented in previous discussions. This survey shows how ML can be applied to reduce road accidents and traffic congestion. CR can be used to accommodate the spectrum required to support massive data communication among automated driverless vehicles and networks. ML can be an integral part of this driverless or automated vehicle system. Similar to a robot, an autonomous vehicle (AV) can learn the surrounding environment and communicate with increased safety, reliability, QoS, and energy efficiency by applying such learning. This paper presents the dynamic usages of ML in CR-VANET elaborately. Several of the benefits of CR in VANETs and ML in VANETs and CR-VANETs are presented in Table 1. B. CONTRIBUTIONS OF THIS SURVEY ARTICLE Many survey articles describe CR, VANET, ML, and CR-VANETs individually or describe a few aspects of their amalgamation. To the best of our knowledge, surveys that cover the usage of ML in CR-VANET...

A Survey on the Current Security Landscape of Intelligent Transportation Systems

“…With the explosive growth in the size and the complexity of VANET, it becomes increasingly challenging to manage such networks. Therefore, the necessity to migrate towards more sophisticated solutions that promote autonomy for analysis and decision making using Artificial Intelligence (AI) [165]- [167]. Machine learning (ML), as a subset of AI, is playing a leading role in the creation of next-generation systems due to the recent success in supporting a wide variety of applications and industries [168].…”

Ad‐hoc, mobile, and wireless networks

“…Joint optimization of the spectrum allocation and other network parameters have been explored in some work to achieve target network performance (eg, high network throughput) 15 . The opportunistic spectrum access capability of CR has been adopted in different wireless and ad‐hoc networks, such as VANETs, 16 and WSNs, 12 all accumulated by the insufficient spectrum due to the coexistence of many networks.…”

Multi-Objective Harris Hawks Optimization Algorithm Based 2-Hop Routing Algorithm for CR-VANET

“…It also allows users to vacate licensed channels when it is re-occupied by licensed users. In resolving VANET's spectrum scarcity problem, CR may play a crucial role [10]. Therefore, CRbased VANET or CR-VANET is a promising technology for addressing road safety, traffic jam, and entertainment issues.…”

“…Instant alternate-routing is essential for smooth communication. That is why CR-VANET routing should be versatile and adaptable [10]. Figure 1 illustrates CR-VANET's routing situation.…”