# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 2 Background
### Vehicular Networks and ML
A paper provides a succinct summary of AI techniques in many elements of urban planning [32]. ML, Cognitive radio, VANET, and Cognitive radio-VANET architectures, procedures, challenges, and unanswered questions are addressed in article [33]. ML techniques' CR-VANET applications are examined. A survey of AI applications in a certain topic and its associated sub-issues is discussed in reference [34]. A study presents a survey of ML methods and their uses and difficulties in forthcoming mobile-based networks [35]. A text discusses an assessment of ML applications in a specific area. It also explores the combination of ML and IoT systems to create smart devices in this field [36]. The investigation conducted in [37] aims to investigate many problems regarding the utilization of freely available information in a particular field within the context of ML techniques. In [38], an examination is conducted to analyze the uses of ML in the area of wireless sensor networks. The research presented in [39] focuses on analyzing various kinds of learning techniques. The article [40] introduces an analysis of research on ML usage for a certain topic, together with the key results of the evaluated publications. The research presents a complete assessment of ML algorithms and their uses in IoT devices within an area. The analysis provides detailed descriptions of the research. The investigation in [41] identifies related issues within a particular field related to network systems. Its next section examines relevant articles, presenting ML and implementation details, such as efficiency and database details. In addition, the study also examines ML methods specific to the area. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Year** & **Technique** & **Used with** & **Objective** & **Future Scope** \\ \hline
[30] & 2019 & ML & IOT & Efficient vehicular networks and related applications discussed & ML for Smart Lighting Systems and Smart Parking applications \\ \hline
[31] & 2020 & CNN & ITS & Efficacy and examined the utilization of CNN in ITS; detailed classifications of the problems & CNN for object detection and analysis \\ \hline
[32] & 2020 & AI, ML, DRL & Cybersecurity, smart grids (SGs), 5G, B5G & Best services of 5G and beyond 5G (B5G) communications, and smart healthcare system & ML and DRL for seamless power supply between different communications \\ \hline
[33] & 2020 & ML & Cognitive radio (CR) VANET & Covering various designs, processes, difficulties, and unresolved inquiries. & Interference and shadowing problem using ML \\ \hline
[34] & 2020 & AI & Distributed smart grid & Distributed intelligent grids; integration of renewable energy resources & Self-healing grid; plug-and-play technique \\ \hline
[35] & 2021 & ML & 6G wireless communication & Addressed B5G/6G network-enabled platforms’ wireless communication & Model Agnostic Meta-Learning (MAML); GANs \\ \hline
[36] & 2021 & ML & IOT & Utilizing ML and IoTs by smart vehicular networks. & Data security \\ \hline
[37] & 2021 & ML & IOT & Context of smart living, smart governance, smart economy, and intelligent transport & DL techniques to solve smart city issues \\ \hline
[38] & 2021 & ML & Wireless Sensor Networks (WSNs)—IoT & ML used with WSN-IoT to address smart city problems & Heart stroke rehabilitation system using ML \\ \hline
[39] & 2021 & ML & Power system & ML techniques described with smart grid power system & Force framework security and control \\ \hline
[40] & 2021 & ML & IoT integrated power system & Potential uses in solving issues plaguing contemporary - power systems & - \\ \hline
[41] & 2021 & ML & VANET security & Address various safety issues inside vehicle communications and security threats & Blockchain-integrated ML solutions \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Past work on VANETs using ML.