# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 6 Simulation Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Nodes** & \begin{tabular}{c} **10 MNs and** \\ **90 ONs** \\ \end{tabular} & \begin{tabular}{c} **15 MNs and** \\ **135 ONs** \\ \end{tabular} & \
\begin{tabular}{c} **20 MNs and** \\ **180 ONs** \\ \end{tabular} &
\begin{tabular}{c} **25 MNs and** \\ **225 ONs** \\ \end{tabular} \\ \hline Average processing time (/ ms) & 468 & 1029 & 1697 & 2681 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average processing time of ORAGC with 500 rounds by increasing the number of nodes in the network. In this paper, five routing algorithms (ETLE [27], HEER [28], EEUC [30], ERP [32], and DCHGA [33]) used in the network clustering process are introduced for comparison with the optimization routing algorithm for green communication (ORAGC) in underground mines proposed in this paper. The comparison of lifetimes is shown in Figure 3. Notably, the ORAGC algorithm saves the most energy in the four different scenarios (as previously described). This result occurs because some routing algorithms, such as the ETLE and ERP methods, cannot address heterogeneous factors, and only one factor is considered in these algorithms. For example, the heterogeneity of the initial energy is the only factor considered in the ETLE. Therefore, if the network connectivity is poor, the collision rate will be high and result in a high probability that retransmission will cause extra energy consumption, and vice versa. The DCHGA considers different heterogeneous factors in the clustering process, but the energy consumption model is not optimized for the heterogeneity of the network. Therefore, the ORAGC improves this approach, and a weight model is established that considers connectivity, energy consumption, and delays based on the DCMST. Figure 2: (**a**) Residual energy in different rounds in scenario 1. (**b**) Residual energy in different rounds in scenario 2. (**c**) Residual energy in different rounds in scenario 3. (**d**) Residual energy in different rounds in scenario 4. The following tables list the rounds when the first and last nodes failed in the network. In comparing the data, the network lifetime can be effectively assessed. To verify the performance of the ORAGC, the five routing algorithms are compared based on the network lifetimes estimated by the simulation experiment. The results in Tables 2 and 3 show that the ORAGC yields the best performance in the four different scenarios. In scenario 1, the first node fails after 1910 rounds, and the last node fails after 14,780 rounds based on the ORAGC. These results are 7.3% and 15.02% better than those of the DCHGA, respectively. In scenario 2, the first node fails after 1690 rounds, and last node fails after 11,130 rounds for the ORAGC. These results are 8.33% and 12.99% better than those of the DCHGA, respectively. In scenario 3, the first node fails after 1830 rounds, and last node fails after 11,130 rounds for the ORAGC. These results are 8.93% and 10.24% better than those of the DCHGA, respectively. In scenario 4, the first node fails after 1640 rounds, and last node fails after 8960 rounds for the ORAGC. These results are 8.93% and 10.24% better than those of the DCHGA, respectively. In addition, the lack of anchor nodes has a larger impact on the network lifetime than does the number of intersections (or lack of LoS). In general, the ORAGC considers the heterogeneous factors in the network and establishes an energy consumption model and routing paths based on the DCMST to initialize cluster processing based on the GA. The results show that the ORAGC exhibited better performance than other routing algorithms for the given GCUM. Figure 3: (**a**) Energy consumption of the routing algorithms in scenario 1. (**b**) Energy consumption of the routing algorithms in scenario 2. (**c**) Energy consumption of the routing algorithms in scenario 3. (**d**) Energy consumption of the routing algorithms in scenario 4.