## 3 Intelligent Cluster-Based Routing Approach
### Cluster Formation
The critical parameters for gauging the system's performance encompass end-to-end delay, network throughput, average hop count/throughput, and average delay. We assessed these parameters by comparing results referenced in the literature [4; 36]. Our comprehensive evaluation unfolds within the expansive confines of the NS-2 simulator, covering 1400 m by 1400 m. The experimental parameters and configurations are meticulously detailed in Table 2. To appraise the performance of our proposed protocol, we scrutinized a custom network topology replete with interaction points, vehicles, highways, RSUs, source vehicles, and destination vehicles. The efficiency of the revised-AODV protocol was assessed based on crucial parameters, including end-to-end delay, network throughput, average hop count, and average delay. These metrics serve as quantifiable indicators, offering a clear basis for comparing AODV approaches. By focusing on these specific factors, the paper establishes a comprehensive framework for evaluation, ensuring a legitimate and justified comparison between the proposed algorithm and existing AODV variations. Furthermore, in our evaluation, we incorporated node mobility and the count of active connections within the network as pivotal parameters for the ultimate test of identifying malicious nodes. Our study's vehicles traversed roadways while communicating with the roadside infrastructure. This communication transpired via Wi-Fi connectivity, enabling vehicles to interact with one another and with RSUs. Such interactions are essential for content requests and subsequent content delivery, governed by specific parameters. Vehicles traveling in various directions and along different routes also established connections with the next RSU and other vehicles within their cluster range. Despite adopting cluster-based routing, each cluster adhered to its unique routing protocols. When a new vehicle entered the range of a cluster and became a member, it solely solicited content from the designated RSU. We employed 100 vehicles in our simulation, randomly distributing their internal distances within a given cluster. The road width was standardized at 3 m, with all roadside elements remaining stationary. This focus on V2V communication rendered the vehicles immobile and exclusively operated within urban roadside areas. The average speeds of these vehicles fluctuated within predefined limits, and the simulation duration, as indicated in Table 2, was extended to surmount challenges, thereby facilitating vehicle travel. We gauged the performance of our model through a set of key performance \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulator & SUMO 1.19 + NS-3.0 \\ Network Area Range & 2500 m \(\times\) 2500 m \\ Vehicle Drive Time & 200 s \\ Total Simulation Time & 1000 s \\ Nodes Density & 100, 150, 200, 250, 300, 350, 400. \\ Wireless Protocol & 802.11 b \\ TransmissionRange Among Vehicles & 1500 m \\ Number of Vehicles & 200 \\ Road Conditions & Two Way Highly Road \\ Number of RSUs & 30 \\ RSU Wireless Area & 250 m \\ RSU Broadcast Time Interval & 50 s \\ Network Connectivity & 5G \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation setup and its range for effective routing and parameter discussion. indicators (KPIs). These included an ANN-based intelligent network model for black hole detection, measuring the training and testing accuracy to minimize error rates in detecting black hole attacks. We also quantified hop count to ascertain the minimum hops in communication paths from source to destination, gauged end-to-end delay by calculating the ratio of packets sent and received at the destination node, and measured network throughput. ### Dataset and Evaluation Results

We employed a supervised deep learning model to identify malicious nodes. This model was meticulously trained using pre-existing datasets, as documented in reference [36], which pertained to detecting malicious nodes within wireless sensor networks.