## 3 Intelligent Cluster-Based Routing Approach
### Cluster Formation
The parameters for cluster formation are \(\mathrm{V_{cl}^{vel}}\) the velocity of vehicles reached in the cluster domain, \(\mathrm{V_{cl}^{loc[x,y]}}\) shows the location of vehicles for the cluster erra transformation, map_lans shows the actual maps location of vehicles, tim_span shows the time tables from the entry into cluster and approval from the ML model, \(RSU_{i}\) all the RSUs used in the clusters, and \(\mathrm{N_{neighbour}}\) shows the parameter for the number of neighbours of the vehicles used in the cluster formation. The aspects involved in this algorithm show the vehicle's final selection and clear understanding. The algorithm outputs the customer and its members with the number of clusters made through the proposed approach. The complexity of Algorithm 1 for cluster formation involves nested loops iterating over time spans and map locations, making it dependent on the size of the input space (tim_span and map_lans). Within these loops, the algorithm performs conditional checks and calculations based on parameters such as vehicle speed, distance thresholds, and the number of neighbors. The number of RSUs, the conditions for cluster formation, and the execution of Algorithm 2 influence the overall time complexity. The complexity is likely polynomial, significantly impacting the nested loops and conditional statements. Input parameters are the key parameters based on which the algorithm selection process works. \(\mathrm{N_{type}}\) is used for the type of ML algorithm used, \(\mathrm{N_{k}}\) is the neural network kernel function, \(\mathrm{ML_{Aleration}}\), shows the number of iterations of an ML model speech, \(\mathrm{ML_{Neurons}^{Quantity}}\) shows the quality of NN neurons in the ML model, data \(-\)\(\mathrm{Division}(25,50,75,100)\) shows the clear division of data into sections, and \(\mathrm{C_{N}^{VN}}\) shows the selected nodes from all cluster nodes. The output parameter is the final selection vehicle that is malicious or non-malicious. The training and testing phases of the machine learning model determine the complexity of Algorithm 2. The training involves iterating over the nodes in the network (All_VN), with nested loops for property allocation, model training, and parameter initialization. While evaluating the trained model, the testing phase involves iterating over the trained nodes and simulating the authentication process. The time complexity is influenced by the number of nodes, neurons in the neural network, and the iterations involved in training. Additionally, the use of deep learning techniques suggests a complexity that is likely high and depends on the complexity of the neural network architecture. ### Path Prediction

Path projection is achieved by implementing an enhanced AODV. In the standard AODV protocol, three operational modes exist:

* **Route Request Mode:** In this mode, a request for a route is disseminated to all neighboring nodes, persisting until it ultimately arrives at the intended destination. * **Route Reply Mode:** In this mode, each neighboring node that can potentially offer a route to the destination responds by providing the route information to the source node. The source node then forwards the message via the route characterized by the most prominent sequence number. * **Route Maintenance Phase:** This phase comes into play when a route fails to transmit a message to the destination. #### 3.5.1 Route Request and Route Reply

In the initial phase, the source node initiates the transmission of a forward communication path (FCP), including a timer, to the neighboring nodes. This path traverses through all nodes until it successfully reaches the destination node. Moving from one node to another, it accumulates and stores data concerning the entire set of visited nodes. Thesedata encompass details such as the identification of the visited nodes, the time of arrival at each node, the distance of each node from the source, and the node's capacity, denoted by the number of channels it possesses.