## 3 Intelligent Cluster-Based Routing Approach
### Cluster Formation
The proposed methodology demonstrates an impressive reduction in end-to-end delay within the network, marked by minimal latency in the transmission of data packets between senders and receivers, quantified in milliseconds. As showcased in Figure 14, this reduction was notably pronounced in scenarios with varying vehicle densities and speeds, where the efficiency of transmitting and receiving packets between sender and receiver vehicle nodes is paramount. Comparatively, other techniques exhibited considerably higher delays within the network, affecting the efficiency and reliability of data delivery. In contrast, the proposed approach consistently performed superiorly, showcasing minimal delays across the network's diverse scenarios. ### Average Hop Count

Average hop count (AHC) is a pivotal network simulation parameter within the VANET context. This metric rationalizes the trajectory of network packets as they traverse from consumer nodes to producer nodes reciprocally. The AHC meticulously tracks the number of neighboring nodes passing by these packets, utilizing the designated simulation parameters. The results rendered by the AHC yield predictive insights into the number of hops required for data transmission from producer nodes to consumer nodes within VANET. Figure 14 vividly illustrates these results, offering a comparative analysis with established network techniques. Furthermore, Figure 15 presents a comprehensive comparison of hop counts with well-known routing strategies such as improved ad hoc on-demand distance vector routing (I-AODV), ad hoc on-demand distance vector routing with load balancing (AODV-L), ad hoc on-demand distance vector routing with reliability (AODV-R), and

Figure 14: Average hop count for proposed methodology in the network. Figure 13: End-to-end delay for packet delivery. ### Average Delay

The \"Average delay\" refers to the time between processing data requests from different networks or zones. We carefully evaluated this \"Average delay\" using simulations with between 50 and 150 trucks. The basis for assessing the results of our suggested system was the following two crucial factors. Figure 17 provides a concise graphical depiction of the results, making it easier to understand the outcomes that our creative method produced. The trends that were observed point to an early rise in the average delay, which is followed by a leveling out of the delay and, finally, a thorough summary of the outcomes. These outcomes clarify the overall approach and the definitive conclusions, which support the effective management of the system. While the material cached in various zones was nonexistent at the beginning of the simulation, it eventually led to the effective use of our suggested methods. We eventually contrasted the findings obtained from our recommended method with those obtained from improved coverage, and connectivity in heterogeneous wireless sensor networks was improved. Routing protocol for coverage and connectivity (RPCC) was proposed in which point coverage and connectivity was enhanced. MLCP maintained a hierarchical topology, where elected cluster-heads at the lower level did not participate in the next cluster-head election. Each member of the high-level cluster came from one lower-level cluster [43]. On-demand routing protocol DSR with a CCMP-AES mode to defend against black hole attacks provided confidentiality and authentication of packets in both routing and link layers of MANET [44]. Payment channel networks (PCNs) were designed and utilized to address the scalability challenge and throughput limitation of blockchains. Routing is a core problem of PCNs. An ideal PCN routing method needs to achieve (1) high scalability that can maintain low per-node memory and communication costs for large PCNs, (2) high resource utilization of payment channels, and (3) the privacy of users [45]. These outcomes significantly improve the suggested approach's operational effectiveness.