## 3 Intelligent Cluster-Based Routing Approach
### Intelligent Path Prediction  
Our devised method operates under the assumption of a uniform dispersion of vehicles across the urban landscape, forming two-dimensional links. The transmission, denoted as Tr, is distributed uniformly throughout the vehicular ad hoc network (VANET) environment. Vehicle \"P\" is regarded as the neighboring node of vehicle \"q\", and the transmissions between these vehicular nodes (V-Nodes) should not exceed a specific range denoted as \"r\". Figure 7 illustrates the methodology of the proposed system. We set the BS as the base station to collect vehicle information and apply ANN-based implementation under a modified AODV algorithm concerning trustworthy nodes. T\({}_{\text{R}}^{\text{N}}\) machine learning makes these trust nodes T\({}_{\text{R}}^{\text{ML}}\). The vehicle requests a route. V\({}_{\text{R}}^{\text{root}}\) for data-sending and then receiving replies through V\({}_{\text{R}}^{\text{root}}\). A uniform speed profile is maintained across all vehicles within our framework. We delineate two primary lanes connecting the source and destination nodes. Each vehicular node (V-node) employs a singular algorithm, affording a solitary opportunity to succeed within the VANET environment. This algorithm facilitates intercommunication between different waypoints. Thanks to our method, minimal interference with the driving behavior of vehicle nodes results in substantial control over other network participants. A routing protocol is employed to adhere to prescribed routing instructions issued by the RSU, as shown in Table 1. Failure to comply with RSU instructions for request or response routing results in non-disclosure of routing and other vital information, ensuring its security. VANET proves instrumental in efficiently delivering and placing content to serve vehicular nodes. VANET augments content request and response parameters by incorporating effective monitoring and control measures. Our proposed methodology is visually represented in Figure 8, encompassing vital components such as malicious node detection through machine learning, cluster formation, and efficient path prediction utilizing a modified AODV protocol. In the initial phase, nodes undergo evaluation by the RSU through a deep learning model. Upon satisfying the requisite clustering parameters, nodes are integrated into clusters, and optimized communication paths are predicted using the modified AODV protocol. This process is elucidated in Figure 8. Modified AODVs (ad hoc on-demanddistance vector) enhance the traditional AODV routing protocol used in mobile ad hoc networks (MANETs). The modifications in this protocol aim to improve the routing efficiency, reduce latency, and enhance its adaptability to the dynamic nature of MANETs. The main reasons for the modification of AODVs are as follows:  
* Enhanced route discovery: Modified AODVs employ advanced algorithms to optimize the route discovery process. It uses techniques like improved route request broadcasting and route reply propagation. This enhancement reduces the time taken to establish a route. * Classification parameter: The modifications introduce a classification parameter that helps categorize routes based on their stability and reliability. This parameter is used to select the most suitable route for data transmission. * Dynamic route maintenance: Modified AODVs adapt to network changes more efficiently. It incorporates mechanisms for monitoring the stability of routes and triggers route updates when necessary. This ensures that the protocol can quickly react to link failures and changes in network topology. * Load balancing: The modified version is designed to distribute traffic evenly across available routes, preventing congestion and improving overall network performance. This is achieved through route selection algorithms that consider the current network load. * Improved scalability: The modifications address scalability concerns by optimizing the route table management.