## 5 Evaluation Results and Discussions  
Evaluation results show that the proposed model outperforms the benchmark approaches [23; 25] in the same simulated network environment. For PDR, the results in both cases improve as the number of nodes is increased. However, given that the position of the nodes is continuously updated upon the successful addition of a new packet, the expected zone is usually small in most of the benchmark cases. This phenomenon prevents the response of the allowed zone to the requested packets. Hence, more data packets may not be sent to the benchmark, especially when the route to the destination node is a multi-hop path. Figure 4 shows the efficiency of OPSO-LAR and its progress relative to the other benchmark approaches. In comparison, we can easily see an improvement in the PDR when the number of nodes is 100 for the proposed model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Used Parameters** \\ \hline
**Network Parameters** \\ \hline Environment dimensions (width \(\times\) height) & 1000 m * 1000 m \\ \hline Node velocity & 20 \(\mp\) 2.5 [m/second] \\ \hline Experiment Duration & 100 s \\ \hline Rate of Logging Data & 10 s \\ \hline Number of Nodes & 65 nodes \\ \hline Max CZR & 250 m \\ \hline Initial battery energy & 17,000 [unit] \\ \hline \multicolumn{2}{c}{**PSO Parameters**} \\ \hline No. of iterations & 1000 \\ \hline No. of particles & 40 \\ \hline Lower boundary of the position of the particle & [0 0 0 0 0.25] \\ \hline Upper Bounds of particle’s position & [1 1 1 1 1] \\ \hline Lower Bounds of particle’s velocity & [\(-\)0.5 \(-\)0.5 \(-\)0.5 \(-\)0.2 \(-\)0.2] \\ \hline Upper Bounds of particle’s velocity & [0.5 0.5 0.5 0.2 0.2] \\ \hline Personal Acceleration Coefficient c1 & 2 \\ \hline Global Acceleration Coefficient c2 & 2 \\ \hline Inertia Coefficient w & 1 \\ \hline Damping Ratio of w & 0.99 \\ \hline Mutation Rate & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parametersA clear benefit of OPSO-LAR over EALAR and DLAR is observed via the measure for overhead. While OPSO-LAR can minimize the number of overhead packets, EALAR and DLAR fail to find the requested routing packet, which can be forwarded to its destination as a result of the narrow-allowed sending area, as shown in Figure 5.  
Regarding the E2E delay performance, the delay diminishes when the number of nodes is increased in both cases. However, our OPSO-LAR prolongs the delay during the sending of the packet to the destination because of the increase in speed and the number of hubs while the calculation of the packet delivers long E2E delay compared with EALAR and DLAR. Figure 6 displays the increase in OPSO-LAR.  
Figure 4: Packet delivery ratio of OPSO-LAR, Energy-Aware Location-Aided Routing (EALAR), and Density Aware Location-Aided Routing (DLAR).  
Figure 5: Overhead of OPSO-LAR, EALAR, and DLAR.  
The energy required to forward one packet is improved in OPSO-LAR compared with EALAR and DLAR. The energy wasted by sending several unnecessary routing requested packets (including control packets) without any significant improvement in the PDR.  
Energy demand is clearly improved when the number of nodes in the network is 100, as shown in Figure 7. The figure shows the consumed energy per packet during the sending of the packets to the destination. Due to the initialization of the solution in the algorithm, the other approach achieves a higher accuracy than the proposed approach when the number of nodes is 50. This result is identical to Figure 4 when the number of nodes is 50 nodes.