## 3 Enabling Technologies and Challenges for Futuristic Vehicular Networks
### Machine Learning
#### 3.3.1 Centralized ML  
Even though VUs are capable of training fairly complex ML algorithms by themselves, it is yet not recommended due to several reasons. First, with their limited resources, training complex ML algorithms over Vehicles Onboard Units (OBUs) will be computationally expansive and can introduce large latency and energy costs. Complex ML techniques such as DNN require a large amount of data during training, which individual VUs are not capable of providing. Furthermore, dynamic environments like VNs are continuously changing, and the surrounding environments can affect the VUs performance while performing ML model training.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**EC Platform** & **Advantages** & **Disadvantages** \\ \hline
**VEC** & Reduced Transmission Distance with Line of Sight (LOS) Communication & Limited Resources, Coverage Range, Frequent Handovers \\ \hline
**TN-EC (i.e., BS)** & Higher Computation and Communication Resources, Better Coverage Range & High Transmission Delay with Degraded Channel Quality (NLOS Communication) \\ \hline
**Cloud** & Unlimited Resources and No Coverage Issues & Huge Transmission Delay, Backhaul Network Congestion, Security Issued Due to Long Distance Communication Channels \\ \hline
**LAP-EC** & Reduced Transmission Distances with LOS communication, Reduced Deployment and Maintenance Time and Costs & Limited Resources, Low Flight Time \\ \hline
**HAP-EC** & Moderate Transmission Distances with LOS communication, Can Have High Resources, Solar Energy Source & High Deployment and Maintenance Time and Costs Compare with LAP, Communication can be Affected by Rain Fading \\ \hline
**Satellite-EC** & High Computation and Communication Resources & Large Transmission Distances (not suitable for latency critical VN), Large Deployment and Maintenance Cost \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of different EC platforms for VN services and applications.  
In the centralized learning approach, several randomly distributed VUs transmit their raw data towards a powerful centralized server (ML-server) with rich communication and computation resources. With this approach, fairly complex ML techniques like DNN can be adapted to solve VNs problems with better performance. With these advantages, some challenges need to be considered while implementing centralized ML-based solutions over vehicular environments. Though centralized computation servers are rich with computational resources, ML model training costs can grow exponentially with the increasing complexity of ML techniques (DNN with a high number of layers), which ensures large computation delays at servers. Furthermore, the transmission of the whole dataset towards centralized servers can be challenging with VUs limited communication resources and dynamic channel environments. In the case of VNs, with changing environment dynamics and corresponding renewed datasets, it is important to update the trained ML models for a short duration of time to avoid issues like model drift. Thus, centralized ML model training approach can have several issues while considering it for solving VNs problems.  
Figure 2 shows an example of a centralized ML model training over VN where centralized ML server collects training data from individual VUs for performing training operations.