## 4 w-SMNO: Performance Analysis
### Simulation Outcomes
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Factors**} & \multicolumn{5}{c}{**Message Delivery Rate (\%)**} & \multicolumn{5}{c}{**Collision Rate (\%)**} \\ \hline
**State-of-the-Art** & **Proposed** & **SES** & **ASM** & **AI-MOFA** & **Proposed** & **SES** & **AISM** & **AI-MOFA** \\ \hline \multirow{5}{*}{**Population (Count)**} & 500 & 73.63 & 68.42 & 63.84 & 65.46 & 10.29 & 16.15 & 17.85 & 16.8 \\ \cline{2-9}  & 600 & 79.48 & 54.59 & 54.91 & 51.27 & 15.08 & 16.03 & 21.22 & 20.46 \\ \cline{2-9}  & 700 & 83.8 & 55.1 & 57.71 & 60.14 & 14.86 & 17.66 & 20.53 & 21.59 \\ \cline{2-9}  & 800 & 84.28 & 58.64 & 63.18 & 65.51 & 14.22 & 21.49 & 19.87 & 19.95 \\ \cline{2-9}  & 900 & 87.56 & 65.86 & 70.5 & 68.7 & 15.87 & 25.68 & 24.11 & 25.52 \\ \cline{2-9}  & 1000 & 94.57 & 70.84 & 76.29 & 69.45 & 16.55 & 29.3 & 31.73 & 30.06 \\ \hline \multirow{5}{*}{**Local Leader Density**} & 10 & 91.09 & 84.06 & 85.23 & 86.45 & 2.043 & 3.153 & 2.761 & 2.523 \\ \cline{2-9}  & 20 & 90.24 & 82.96 & 84.03 & 85.5 & 2.883 & 4.513 & 4.011 & 3.543 \\ \cline{2-9}  & 30 & 89 & 81.53 & 79.24 & 84.21 & 4.003 & 6.603 & 6.071 & 5.263 \\ \cline{2-9}  & 40 & 87.56 & 79.82 & 80.99 & 82.71 & 6.273 & 10.543 & 9.751 & 8.313 \\ \cline{2-9}  & 50 & 88.42 & 77.89 & 79.19 & 80.96 & 9.383 & 15.413 & 14.321 & 12.253 \\ \cline{2-9}  & 60 & 85.73 & 75.68 & 77.03 & 81.33 & 13.493 & 21.673 & 20.291 & 17.373 \\ \cline{2-9}  & 70 & 81.81 & 73.02 & 74.5 & 76.45 & 18.753 & 28.653 & 26.901 & 23.183 \\ \cline{2-9}  & 80 & 79.41 & 70.18 & 71.8 & 73.77 & 25.073 & 36.523 & 34.191 & 30.043 \\ \hline \multirow{2}{*}{**90**} & 76.7 & 67.18 & 68.8 & 70.8 & 32.543 & 46.363 & 43.231 & 38.173 \\ \cline{2-9}  & 100 & 73.66 & 63.21 & 65.01 & 67.12 & 41.073 & 57.633 & 53.641 & 47.633 \\ \hline \end{tabular}
\end{table} 
Table 7: _Cont._Figures 13-24 present the acquired results. The analysis reveals a remarkable enhancement in the reliability of the w-SMNO dissemination approach, manifested in a substantial decrease in the end-to-end delay and the rate of collision of packets alongside an augmentation in both network coverage and the message delivery rate to destinations. These improvements are notably superior to the existing state-of-the-art solutions. As the population size increases, the subgroups allocate members more effectively, thus improving the rate of message delivery, reducing the numbers of collisions, and enhancing the network coverage. Simultaneously, the early identification of relays further reducing the end-to-end delay. Overall it is promoting efficient data dissemination in vehicular communication across the entire network. These insights underscore the effectiveness of our approach, showcasing its potential for real-world deployments. The behavior of the w-SMNO, as shown in Figures 13-16, exhibited sufficient improvement in efficiency over time, especially concerning the defined key metrics of network coverage, the end-to-end delay, the message delivery rate, and the collision rate. As time progressed, the algorithm demonstrated a significant enhancement in network coverage, ensuring a wider span of effective communication. Similarly, the delay factor for the proposed method was reduced satisfactorily, as the time taken for both node processing due to local groups' formation and the identification of an effective relay node was reduced. Finally, from the simulation outcomes for delays, it can be concluded that the time taken for information to traverse from a source to a destination showcases a favorable trend with time, indicating an optimized and expedited data dissemination process. Moreover, the message delivery rate exhibited a positive correlation with time, signifying an increasingly successful transmission of messages within the network. Simultaneously, the collision rate, representing instances of data packet conflicts, demonstrates a decreasing trend over time, underlining the algorithm's adeptness in mitigating collisions and optimizing resource utilization.