# A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks
## 3 Approaches behind the Proposed Protocol
### K-Means  
This approach focuses on the centroid, where all the clusters are connected. The major goal is to minimize the data point distances and their consequent clusters. It takes the simple dataset as input, separates it into k-number of clusters, and reiterations the procedure until it does not determine the optimal clusters as presented in Figure 2. The k-means clustering primarily executes two tasks:  
* Find the optimal value for K by an iterative procedure.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref. ID.** & **Proposed Approach** & **Issues Addressed** & **Performance Parameters** \\ \hline
[21] & Threshold-based clustering & Network instability to maximize the network efficiency & E2E delay and throughput \\
[22] & Artificial bee colony & Quality-of-service in VANET routing & E2E delay and PDR \\
[23] & Hybrid bee swarm & Timely dissemination of messages to improve & Average E2E delay and PDR. \\
[24] & Weight-based & Delay constraints in VANET & Average E2E delay and PDR. \\
[25] & Ant colony Optimization & Effective bandwidth utilization, scalability, & E2E delay and PDR \\
[26] & Artificial bee colony & Find optimal routes based on QoS requirements & PDR, E2E delays, and the network overhead \\
[27] & Harmony search algorithm & Flexible routing due to the dynamic nature of VANET & PDR and network overhead \\
[28] & Particle swarm optimization & Scalability and overhead for routing & Delay, the routing load, dropped packets, \\
[29] & Support vector machine & Generate routing metrics to enhance reliability & throughput, and PDR \\
[30] & Machine Learning & Reduce communication delays and enhance the & PDRs and transmission delays. \\
[31] & Q-learning & Unreliability of the link due to vehicle movement & Package delivery rate and E2E delay \\
[32] & K-Harmonic Means & Multi-hop & Packet delivery rate \\  & reliability and efficiency & & \\ \hline \end{tabular}
\end{table}
Table 1: Evaluation of surveyed protocols in VANET.  
* Allocates each data input to its nearby k-center and generates a cluster.
* A k-means algorithm recognizes influential nodes from each cluster with the probability of achieving energy-efficient transmission. In various modifications, the k-means groups the vehicles and selects any nodes in some rounds as CHs. It can decrease the amount of communicated messages from one node to another, saving the network more resources. A k-means clustering algorithm in which dynamic grouping by k-implies is performed that fits well with the vehicular network's dynamic topology characteristics. The suggested clustering reduces overhead and traffic management. Therefore, every cluster contains data points with unities that do not belong to other clusters. So, the k-means clustering methods [37; 38; 39] have been utilized effectively to resolve various VANET issues. Firstly, k-means algorithm is presented for the clustering of nodes and then a dynamic routing protocol is implemented to obtain results of proposed routing protocols, which are compared with the results of existing techniques as represented in Section 4.