# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## 6 Simulation Verification
### Analysis of Simulation Results  
In the fixed-layout scenario simulations, the load balance of terminal nodes within different groups is illustrated in Figure 10. The optimization degree of node load balance varies across different simulation groups due to terminal nodes' differing numbers and positions carrying monitoring tasks. When the number of business nodes is large, more terminal nodes in the network act as relay nodes to transmit business data, thereby improv \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Business Type**} & **Transmission** & \multirow{2}{*}{**Reliability**} & \multirow{2}{*}{**Success Rate**} & **Business** & \multirow{2}{*}{**Payload**} & \multirow{2}{*}{**Priority**} \\  & **Delay** & & & & **Proportion** & \\ \hline Anomaly alarm & Low & High & 98\% & 5\% & 5 Byte & 1 \\ Control & Low & High & 95\% & 20\% & 10 Byte & 2 \\ Energy monitoring & Medium & Medium & 90\% & 40\% & 20 Byte & 3 \\ Environmental monitoring & Medium & Medium & 80\% & 30\% & 20 Byte & 4 \\ Other business & High & Medium & 80\% & 5\% & 10 Byte & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter settings for terminal node business types \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Parameter Name**} & **Parameter Value** \\ \hline Scenario spatial size & 100 \(\times\) 50 \(\times\) 18 \\ Number of end nodes & 60–200 \\ Number of aggregators & 1 \\ Number of simulations & 20 \\ BW/kHz & 125 \\ SF & 7–12 \\ Coding rate CR & 4/5 \\ Hello message interval/s & 5 \\ TC message interval/s & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameter settings. ing the load balance optimization degree of the LB-OLSR algorithm. In fixed-node-number scenario simulations, it is observed that the load balance achieved using the LB-OLSR algorithm is superior to that of the OLSR algorithm across different groups. This indicates that the load-balancing routing optimization method proposed in this study significantly improves load balance. In the fixed-node-number scenarios, the load balancing of terminal nodes across different groups is shown in Figure 11. Random changes in node positions are made within each simulation group based on the fixed-layout scenario, with 20 simulations conducted to obtain the average result. This approach mitigates the impact of randomly generated business nodes and randomly distributed terminal nodes on the experimental results, thus improving result reliability. In these simulations, the load balancing of nodes utilizing the LB-OLSR algorithm remains stable between 0.2 and 0.4, indicating superior performance compared to the OLSR algorithm, which fluctuates between 0.7 and 0.9. The impact of terminal node position distribution leads to variations in load balancing, but overall, the load balancing tends towards stability. Figure 11: Node load-balancing degree in fixed-node-number scenario. Figure 10: Node load-balancing degree in fixed-layout scenario. In the fixed-node-number scenarios, the remaining energy balance of nodes achieved by the LB-OLSR algorithm is significantly lower than that of the OLSR algorithm, indicating that the proposed method in this section results in a more balanced distribution of residual energy among terminal nodes. In various experimental groups, fluctuations in the balance of node residual energy were observed, which is likely attributed to the random distribution of terminal node positions.The experimental results are shown in Figure 12.  
In the mixed-scenario simulation experiment, 20 terminal nodes are incrementally added to the fixed-node-count scenario, and 20 simulations are performed for each increment. The average result is taken to mitigate the impact of randomly generated business nodes and the random distribution of terminal nodes on the experimental results.