## 3 Intelligent Cluster-Based Routing Approach
### Cluster Formation
Our dataset encompassed 14 independent variables and a singular dependent variable. The independent variable attributes included data such as destination IP, next hop, hop count, vehicle information, RSU's, ANN algorithm, parameters, malicious node, RSU-based decision, route request, route reply, sequence number, classification, packets delivered, and shortest path (yes/no). In this case, the output variable pertained to the class or category of the nodes and the shortest path for the variable values. Our dataset was substantial, consisting of more than 50,000 records. We employed an ANN classifier to detect malicious nodes. Table 3 provides the layers with output parameters and values from each NN layer in the system. Figure 9 shows NN, input, hidden, and output layers. In the context of network routing and decision-making, the configuration of a neural network plays a pivotal role in optimizing performance. A neural network designed for this purpose may comprise, for instance, 14 input nodes representing relevant network parameters, two hidden layers containing 8 and 6 nodes, respectively, and a single output node for decision output. This architecture enables the model to capture intricate patterns and relationships within the data, facilitating complex decision-making processes. The choice of layers and nodes is informed by the specific requirements of the routing task to balance model complexity and computational efficiency. Moreover, leveraging activation functions such as the rectified linear unit (ReLU) and employing dropout techniques at 0.2 between layers enhances the network's ability to generalize and prevent overfitting. Such a well-configured neural network demonstrates the capacity to learn and adapt to dynamic network conditions, ultimately contributing to efficient and adaptive routing and decision-making in complex networking environments. Our approach adopted gradient-boosted neural networks (GBNN) as an ANN model. This hybrid approach combines the strengths of gradient boosting and neural networks. It trains a neural network to capture complex patterns in the data and integrates it with a gradient-boosting framework. GBNN is effective for improving predictive performance on structured tabular data. The gradient-boosted neural network (GBNN) model presents a compelling fusion of the strengths derived from gradient boosting and neural networks, offering a unique set of advantages compared to standalone models. One notable advantage is the model's ability to capture intricate patterns within structured tabular data. By training a neural network within a gradient-boosting framework, GBNNs can effectively learn complex, non-linear relationships that may exist among different features. This is particularly advantageous in scenarios where traditional linear models may fall short, as \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Dense (Dense) & (None, 14) \\ Dense\_1 (Dense) & (None, 64) \\ Dense\_2 (Dense) & (None, 64) \\ Dense\_3 (Dense) & (None, 64) \\ Dense\_4 (Dense) & (None, 1) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NN model with layer parameters and output values. the neural network component allows for extracting high-level abstractions and representations from the data. Another significant advantage lies in the ensemble nature of GBNNs. Integrating gradient boosting with neural networks results in a hybrid model that leverages the strengths of both approaches. The boosting framework helps to improve the model's overall predictive performance by sequentially correcting the errors made by the neural network component. This collaborative learning process often leads to enhanced generalization capabilities, making GBNNs well-suited for a wide range of tabular datasets. We gauged the performance of this classifier using the BHT dataset, as documented in reference [39]. This evaluation was predicated on the forecasted outcome values, and Table 4 comprehensively depicts this anticipated dataset's overall performance.