# Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network
## 4 Simulation Experiments  
To verify the performance of the proposed ACER routing algorithm, the field environment was simulated, and the algorithm was simulated by setting up an application scenario. Each iteration represents one random movement of nodes. The performance was analyzed regarding link quality, network connectivity, and the number of surviving nodes and was compared with the FFACM algorithm and the algorithm in the literature [36]. The simulation hardware equipment is as follows:  
* CPU processor: 15-10400F @ 2.90 GHz;
* RAM memory: 8 GB;
* Hard disk: 1 TB;
* Software platform: NS 2. To ensure the simulation results' validity, each index's results are the average of ten simulations. The main simulation parameters are shown in Table 1.  
Initially, 100 network nodes were generated for simulation in the 3D scene, as shown in Figure 6. The four different colors and shapes of nodes represent nodes randomly distributed on four different horizontal floors. The \(Z\)-coordinates of the nodes are 75, 150, 225, and 300, respectively, to simulate the nodes at different floor heights. There are 25 priority 0, priority 1, priority 2, and priority 3 nodes, and the average number of neighboring nodes is about 8. Communication with nodes not in the neighboring node-set must be done through a multi-hop link. The test scenario does not have obstacles, and the regional distribution density of nodes on each floor is shown in Figure 7. In this thesis, the MAC and IP layers are fused to give each node a fixed and unique identity. The initial node sends connection requests to other nodes in the network with maximum power and establishes connections. The original topology diagram and the topology diagram, after running the topology optimization algorithm, are shown in Figure 8.  
Figure 7 shows the distribution density of construction robot nodes on different floors in a 300 \(\times\) 300 area. The nodes on each floor are not uniformly distributed but are more scattered in the application scenario based on their task requirements. From the comparison of the two figures in Figure 8a,b, we know that after running the topology optimization algorithm, the links with quality below the threshold are eliminated, the topology redundancy is reduced, and the network performance after topology sparse is subsequently investigated. The connectivity rate is characterized by the ratio of the number of nodes in the maximum connected subgraph to the total number of nodes in the current network. As the number of nodes increases from 10 to 100, the connectivity rate curves of the ACER algorithm and the other two algorithms are derived for different numbers of nodes, as shown in Figure 9. The change curve of the connectivity rate can be described as follows, as the network scale continues to expand and the network connectivity rate continues to decrease. Finally, the connectivity rate of the FFACM algorithm and reference [36] algorithm were 93% and 92%, respectively. In comparison, the connectivity rate of the \begin{table}
\begin{tabular}{c c} \hline Node distribution area & 300 \(\times\) 300 \(\times\) 300 m \\ \hline Number of mobile nodes & 100 \\ \hline Initial energy of the node & 1 J \\ \hline Node communication radius & 100 m \\ \hline Maximum node movement speed & 2 m/s \\ \hline Maximum node dwell time & 5 s \\ \hline Unit packets & 4000 bit \\ \hline Network Protocols & IEEE 802.11 \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation parameter setting. The variation in the average link quality is shown in Figure 10. The ACER algorithm improves the topology for link maintenance time and node priority. The routing path quality index is integrated with the routing selection to select more stable and effective links. In the link quality comparison, the ACER algorithm is better than the FFACM and reference [36] algorithms. To verify the effectiveness of the ACER routing algorithm, the following analysis was performed in terms of the number of surviving nodes at different movement speeds of the construction robot nodes.