# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 7 Conclusions  
Green communication is introduced as an advanced approach to underground mine WSNs, which are comprised of numerous nodes with heterogeneous initial energies, data processing capabilities, and functions. The ORAGC is proposed in this paper to achieve the goals, including saving energy, satisfying the requirements of extending the network's lifespan, and function implementation. To adapt to a variety of heterogeneous factors in the network, weight and routing paths are concurrently introduced based on the DCMST. Then, an energy consumption model is established with many heterogeneous factors in the network. Based on the former routing paths, the GA is introduced to initialize the clustering process and balance the energy consumption and longevity of the network. Since different heterogeneous factors are considered, nodes in the network must be identified to create an optimized routing path with a low collision rate and low energy consumption. The ORAGC improved the network lifetime by 7.3% to 8.93% based on the failure of the first node and 15.02% to 23.93% according to the failure of the last node.  
[PERSON] conceived and designed the work that led to the submission, acquired data, and drafted the manuscript. [PERSON] revised the manuscript. [PERSON] and [PERSON] acquired funding. [PERSON] and [PERSON] did experiments and acquired data. [PERSON] provided methods of guidance in the manuscript and played an important role in interpreting the results.  
**Funding:** This research is supported in part by grants from the National Natural Science Foundation of China (61301114, 51304058), the Fundamental Research Funds for the Central Universities, and ANHUI Province Key Laboratory of Affective Computing & Advanced Intelligent Machine, Grant No. ACAIM180102.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Rounds When the First Node Fails (Rounds)**} \\ \hline
**Techniques** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\ \hline ETLE & 730 & 580 & 640 & 520 \\ HEER & 1720 & 1520 & 1670 & 1450 \\ EEUC & 870 & 640 & 780 & 570 \\ ERP & 1310 & 1120 & 1220 & 990 \\ DCHGA & 1780 & 1560 & 1680 & 1510 \\ ORAGC & 1910 & 1690 & 1830 & 1640 \\ Improvement & 7.30\% & 8.33\% & 8.93\% & 8.61\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Running rounds when the first node fails of six algorithms in four different scenarios, and the improvement is given.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Rounds When the Last Node Fails (Rounds)**} \\ \hline
**Techniques** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\ \hline ETLE & 1520 & 1290 & 1440 & 1240 \\ HEER & 2850 & 2590 & 2740 & 2490 \\ EEUC & 2190 & 2010 & 2120 & 1900 \\ ERP & 3120 & 2930 & 3030 & 2780 \\ DCHGA & 12,850 & 9850 & 11,230 & 7230 \\ ORAGC & 14,780 & 11,130 & 12,380 & 8960 \\ Improvement & 15.02\% & 12.99\% & 10.24\% & 23.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Running rounds when the last node fails of six algorithms in four different scenarios, and the improvement is given.