## 4 w-SMNO: Performance Analysis
### Simulation Organization  
Simulation was conducted to demonstrate the effectiveness of the proposed w-SMNO, over specifically based estimation scheme (SES) [43], adaptive intersection selection mechanism using ant colony optimization (AISM) [44], and the adaptive jumping multi-objective firefly algorithm (AJ-MOFA) [16]. A comparative analysis of these are presented in Table 2.  
For extensive experiments, NS2.35 served as the simulation platform. We employed the 500 m \(\times\) 500 m heavy traffic area along Jaipur JLN Marg, India, sourced from OpenStreetMap, as depicted below in Figure 9, to generate a node density trace file using SUMO and MOVE. This file captures realistic mobility environments, including information on each node's speed and location, which are then utilized via NS2.35 as a dataset to assess the performance of w-SMNO. In each simulation, the generated population set is divided into local groups ranging from 5 to 40, each containing populations ranging from 30 to 60 nodes. These groups, each containing populations, are introduced at specific time intervals to assess performance metrics, as explained in later sections. Nodes with speeds ranging from 20 to 100 km/h are included in the population to demonstrate the correlation between the results and a comparative analysis of UDP transport with CBR traffic, with a maximum flow rate of 100 vehicles. Table 3 provides an overview of the simulation parameters utilized in this approach. Figure 8: Threshold instances. The effectiveness of the w-SMNO approach when compared to the non-grouped conventional method to variable subgroups (number of LL) is demonstrated in Figure 10. The outcomes for various metrics are presented for a predetermined number of local groups. As local groups and the vehicle density increase, the effectiveness of w-SMNO undergoes an improvement when contrasted with non-grouped communication. This is because there is less repetition of similar information among nodes in the same group. Breaking down the population into multiple groups helps simplify the process of identifying relay nodes, making it less complex by handling fewer nodes within each group. The results further illustrate that the efficiency of the approach may fluctuate with the rise in the number of groups more than the limitation relative to the vehicle density or population, potentially affecting the overall performance. Table 4 outlines the multiple considered conditions for the evaluation and illustration of the same. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Protocols** & **SES** & **AISM** & **AJ-MOFA** & **Proposed w-SMNO** \\ \hline
**VANET domain** & Routing & Routing & Routing & Routing \\
**Optimization strategy** & Social spider & Ant colony & MO firefly & Spider monkey \\
**Sub-strategy** & Clustering & None & Clustering & ML model \\
**Source identification** & Position-based & Position-based & Position-based & Position-based \\
**Route configuration** & Yes & Yes & Yes & Yes \\
**Route selection** & Yes & Yes & Yes & Yes \\
**Disconnection rate** & Medium & Medium & Low & Low \\
**Route selection approach** & Vehicle link & Metrics & Pareto method & Weighted metrics \\
**Vehicle density (Max)** & 100 & 300 & 100 & 100 \\
**Network connection** & V2V & V2I & V2V & V2V \\
**Comparison** & CLPSO, GA, ACO & GeoSVR, RAGR, EGSR & CPB, NSGA-II & SES, AISM, AJ-MOFA \\
**Environment** & Urban & Urban & Urban & Urban \\
**Simulation tool** & MATLAB & NS2.34 & MATLAB & NS-2.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison analysis between the SES, AISM, AJ-MOFA, and w-SMNO protocols. Figure 9: Road segment (Jaipur JLN Marg, India).