## 4 w-SMNO: Performance Analysis
### Simulation Outcomes  
This subsection presented the simulation outcomes of this study, illustrating the efficacy of the proposed approach. With this methodology, numerous experiments were conducted, and the system's performance was assessed across diverse scenarios through extensive simulations. To scrutinize the necessity of total local leaders (\(LL\)) in each subgroup for a random population, Experiment 1 was conducted, involving the variation in both \(LL\) and the population size over simulated time. Figure 11 demonstrates the analytical behavior of the experiment through a probabilistic approach for \(LL\) with a population size through a simulation. To the specific members limit, the number of subgroups are directly proportional to the monkey population. The analysis outcomes clearly represent the requirement for balance in \(LL\) with the increases in the population for the effective functioning and optimization of various systems, especially in scenarios involving large-scale networks. Careful consideration and adjustment of subgroup values become pivotal to achieving optimal performance and maintaining a harmonized operation as the number of nodes in the network grows. Moreover, we assessed the stability of the proposed system by examining the various arrangements of both the minimum and maximum values for the \(LL\) and the population size in Experiment 2. Table 5 illustrates the groups with the combination of minimum and maximum values for \(LL\) and \(P\) for the cases of experiment. The results of the experiment are visually depicted in Figure 12.  
Figure 11: Experiment 1: probability analysis for LL with population size. While the stability of the system in networks with the maximum population and \(LL\) was relatively diminished compared to networks with the minimum \(LL\) and the maximum population, it is important to note that, concurrently, the overall performance of the w-SMNO method demonstrated a noteworthy enhancement. Opting for the minimum local leader (\(LL\)) value, coupled with the maximum population, proves advantageous when compared to other combinations. This choice minimizes the need for relay candidates, leading to a more streamlined and efficient system. The network exhibits increased stability, and the overall processing time is significantly reduced. With fewer relay candidates to manage, the system experiences enhanced efficiency and reliability, making the combination of the minimum \(LL\) and the maximum population a favorable configuration for optimal performance. A comprehensive examination was undertaken through Experiment 3, concentrating on performance metrics like network coverage, the message delivery proportion, the end-to-end delay, and the collision rate as defined earlier to demonstrate the superiority of w-SMNO. This investigation involved a varying spider population (vehicles) and simulation time while maintaining a constant number of subgroups percentage (\(LL\)) to 5, 25, and 35 as an average of the minimum and maximum, with an initial population size of 50 nodes. Members of subgroups were allocated through the member allocation algorithm, as discussed in previous sections. The simulation result for the same is shown in Tables 6 and 7. Figure 12: Experiment 2: w\(-\)SMNO network stability under (min, max) group. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Spider Monkey (Vehicles)**} \\ \cline{2-3}  & **Groups** & **Population** \\  & **(LL)** & **(P)** \\ \hline A (min, min) & 10 & 600 \\ B (min, max) & 10 & 1000 \\ C (max, min) & 40 & 600 \\ D (max, max) & 40 & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Network stability under (min, max) group.