## 12 Simulation and Results
### Impact Analysis of Scheduled and Unscheduled Nodes  
Figure 10 presents a detailed comparative analysis of the impact of node scheduling on three critical performance metrics in a network: Packet Delivery Ratio (PDR), End-to-End delay, and energy consumption. The analysis is carried out for both scheduled and unscheduled nodes using five optimization algorithms: Bacterial Foraging Algorithm (BFA), Firefly Algorithm (FA), Levy Firefly Algorithm (LFA), Genetic Algorithm (GA), and Binary Particle Swarm Optimization (BPSO). In Figure 10(a), the graph illustrates the Packet Delivery Ratio (PDR) as a function of the number of nodes in the network. As the number of nodes increases from 100 to 700, the PDR improves for both scheduled and unscheduled nodes. However, it is evident that the scheduled nodes consistently exhibit a higher PDR compared to unscheduled nodes. This implies that node scheduling plays a significant role in optimizing the PDR, which directly impacts the reliability of the network. The PDR for scheduled nodes reaches around 95.5% as the number of nodes increases, while unscheduled nodes only achieve around 80%. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Benchmark Schemes**} & \multicolumn{6}{c}{**Performance Parameters**} \\ \cline{2-9}  & **AE2E** & **RO** & **RREQ** & **PDR** & **Localization Error** & **Energy Consumption** & **RREQ and RO** & **Metaheuristic Enabled** \\ \hline RDBF & 0.3\% & 6.2\% & 6.5\% & 89.2\% & 21\% & 88,000 J & No & No \\ \hline EEPDBR & 0.45\% & 4.1\% & 4.2\% & 93.7\% & 27\% & 83,000 J & No & No \\ \hline UDB & 0.23\% & 6.1\% & 6.1\% & 94.9\% & 19.6\% & 81,000 J & No & No \\ \hline
3 DUL & 0.37\% & 4.5\% & 4.5\% & 93.0\% & 17\% & 87,000 J & No & No \\ \hline UL & 0.39\% & 4.1\% & 4.1\% & 93.8\% & 15\% & 90,000 J & No & No \\ \hline RSMB & 0.4\% & 3.9\% & 3.9\% & 94.5\% & 20\% & 81,000 J & No & No \\ \hline Proposed & 0.2\% & 2.1\% & 2.2\% & 95.5\% & 15\% & 72,000 J & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of different baseline schemes with proposed approach under different parameters. Figure 9: Performance analysis for (a) localization error and (b) energy consumption. Figure 10b shows the effect of node scheduling on End-to-End delay. Here, we observe that scheduled nodes significantly outperform unscheduled nodes in terms of reducing delay. As the number of nodes increases, both scheduled and unscheduled nodes experience a reduction in delay. However, scheduled nodes demonstrate a much steeper decline in delay, reaching below 3 s when there are 700 nodes. In contrast, unscheduled nodes still experience higher delays, approximately 5 s, even as the number of nodes increases. This highlights the efficiency of node scheduling techniques like BFA, FA, GA, and BPSO in minimizing the latency within the network. Lastly, Figure 10c presents the energy consumption trends for scheduled and unscheduled nodes. As the number of nodes increases, energy consumption initially rises for both scheduled and unscheduled nodes due to higher network traffic and processing requirements. However, the energy consumption for scheduled nodes is consistently lower than for unscheduled nodes. At the peak, the energy consumption for unscheduled nodes reaches nearly 8000 Joules, while scheduled nodes consume around 7000 Joules. This reduction in energy consumption through scheduling is essential for prolonging the network's operational lifetime, particularly in energy-constrained environments such as UASNs [40]. Overall, the figure demonstrates the effectiveness of scheduling techniques such as BFA, FA, LFA, GA, and BPSO in optimizing network performance. Scheduled nodes outperform unscheduled nodes across all three-performance metrics, PDR, End-to-End  
Figure 10: Impact analysis of (**a**) Packet Delivery Ratio; (**b**) End-to-End delay; and (**c**) energy consumption for scheduled and unscheduled nodes in network.