# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 6 Simulation Results and Analysis  
In this paper, to achieve the goal of green communication, an effective method is established to balance the network load and energy consumption of mobile nodes in the network. After a certain number of rounds, the energy consumption of nodes in the network is balanced to prevent some nodes from prematurely failing, which negatively affects the lifetime of the entire network. To achieve this goal, simulation experiments involving the GCUM were conducted using the MATLAB(MathWorks, Natick, MA, USA) simulation platform by a desktop computer with Intel Core i5-7500 @ 3.40 GHz Quad Core, 8 GB DDR4 2400 MHz memory, and the Windows 7 operating system. The ORAGC has two steps of calculation with DCMST and GA. Thus, the time complexity \(O\) is the sum of these two algorithms described as:  
\[O=O(E*\lg E)+O\Big{(}n^{2}\Big{)}\]  
where \(E\) is the number of edges in the DCMST and \(n\) is the number of nodes in the network. The environmental parameters and structure were defined as follows. 1. The experimental area is simulated as tunnels in the GCUM, in which there are long passages and intersections. Anchor nodes are deployed with the same interval distance of 50 m in tunnels. 2. There are two types of mobile nodes in the GCUM: ordinary nodes and multifunction nodes. Multifunction nodes comprise 10% of the total number of mobile nodes in the network. 3. In a normal scenario, anchor nodes serve as cluster head nodes when the cluster process is initiated. In a special case, such as an accident, mobile nodes, including both ordinary and multifunction nodes, can replace anchor nodes as cluster head nodes. 4. In the GA algorithm, the population size is 30. The performance of the final optimization may be negatively impacted by a high probability of mutation. To compare fairly in the later experiments, we used the same probability of mutation and crossover as DCHGA and other algorithms. Thus, the mutation probability \(\beta\) is set to 0.006. In addition, the convergence results will exhibit better correlation at low probabilities of crossover. Thus, the crossover probability \(\alpha\) is set to 0.8. 5. The transmission ranges of anchor, multifunction and ordinary nodes are 50 m, 30 m and 15 m, respectively. The simulation parameters are set by 100 mobile nodes, including 10 multifunction nodes and 90 ordinary nodes, which are deployed in the simulation area. Respectively, the initial energy of ANs, MNs, and ONs is set as 10 J, 1.5 J, and 0.5 J, and the energy consumption at idle is 50 nJ/bit. In addition, packet size is set as 300 bits, and thresholds \(\delta_{0}\) and \(\delta_{1}\) are set as 0.4 J and 1 J. Table 1 shows the average processing time of ORAGC. Figure 1(a) shows scenario 1 in a long tunnel without intersections where anchor nodes are deployed at intervals of 50 m. Figure 1(b) shows scenario 2 in a long tunnel without intersections where anchor nodes are deployed at intervals of 200 m to simulate that some anchor nodes were damaged in an accident. Figure 1(c) shows scenario 3 in an area of tunnels with a large number of intersections where anchor nodes are deployed at intervals of 50 m. Figure 1(d) illustrates scenario 4 in an area of tunnels with a large number of intersections where anchor nodes are deployed at intervals of 200 m to simulate that some anchor nodes were damaged in an accident. As shown in these figures, the algorithm performance is best for scenario 1 because the network comprises long tunnels without blockages and transmission occurs based on line of sight (LoS). In scenario 2, due to the presence of intersections, some transmissions cannot be directly processed, and multihops that are forwarded by certain mobile nodes must be used. This process results in higher energy consumption. Therefore, in scenarios 3 and 4, more energy consumption is required because LoS exists and fewer anchor nodes are available. In general, these conditions will increase energy consumption in GCUMs.