# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## 6 Simulation Verification
### Analysis of Simulation Results
In these simulations, the node load balancing under different terminal node counts is illustrated in Figure 13. The load balancing of nodes using the LB-OLSR algorithm gradually decreases and stabilizes as the number of terminal nodes increases. Conversely, the load balancing in the OLSR algorithm mostly remains between 0.9 and 1, showing no significant fluctuation with an increasing number of terminal nodes. When the number of terminal nodes is low in large-scale buildings, their spatial distribution is sparse, and the distance between terminal nodes is large. Consequently, the choice of relay nodes to transmit business data from the business nodes to the gateway is limited, significantly impacting the load balancing of the LB-OLSR algorithm. However, when the number of terminal nodes is high, the spatial distribution becomes dense, and the distance between nodes is shorter, providing ample relay nodes. This reduces the impact on route optimization. In the mixed-scenario simulation experiment, the remaining energy balance of the nodes in the LB-OLSR algorithm gradually stabilizes at 0.65. This stability is attributed to the dense spatial distribution of terminal nodes within large buildings when the number of terminal nodes increases beyond a certain threshold, resulting in an ample selection of relay nodes and thereby reducing the impact on routing planning. Conversely, in the OLSR algorithm, as the number of terminal nodes increases from 60 to 120, the failure to consider node load and occupancy rates during the planning of transmission paths for service nodes leads to excessive loads on certain nodes, resulting in a noticeable increase in the remaining energy balance among the nodes.The experimental results are shown in Figure 14.  
Figure 12: Remaining energy balance of nodes in fixed-node-number scenario. The average node load duration under different terminal node counts is depicted in Figure 15. Compared to the OLSR algorithm, the average node load duration is longer under the LB-OLSR algorithm. This is because the algorithm focuses on optimizing the nodes' average load rate and load balancing. As a result, the chosen transmission paths to the gateway may not always have the most minor hops or the shortest distance, which can increase the number of times business data are forwarded. On the other hand, the OLSR algorithm uses the minimum hop count as the routing criterion, resulting in a shorter average node load duration. As the number of terminal nodes increases, more relay node choices become available for business data forwarding, potentially reducing hop count and shortening transmission paths, thereby decreasing the average node load duration. The average business delay under different terminal node counts is shown in Figure 16. The LB-OLSR algorithm results in a shorter average business delay than the OLSR algorithm. The proposed load-balancing algorithm comprehensively considers communication delay, link reliability, node occupancy rate, and node load rate. It dynamically adjusts traffic distribution strategies based on node load conditions and routing performance within the network, thereby avoiding congestion and node load imbalance and reducing queuing delays for business data. As the number of terminal nodes increases, the load-balancing  
Figure 11.0 Remaining energy balance of nodes in mixed scenario. Figure 11.1 Node load-balancing degree in mixed scenario  
algorithm can more effectively utilize these nodes to share the load, accelerating business data processing and reducing business transmission delays.