# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 5 Experimental Analysis
### Experimental Result Analysis  
In the experiment, the result of the greedy algorithm is used as its fitness benchmark (0) to minimize the impact of randomness on the experiment. In each experiment, the fixed graph and data packet input are run 10 times, and the average value of the fitness of the corresponding iteration times is taken as the result of the fitness of the 10 experiments. When the number of points is 10 and the number of packets is 100, the experiment is carried out. The results are shown in Figure 4. Although the initial population using the Dijkstra algorithm is poor, the initial convergence is extremely fast, and a better solution can quickly be found. The convergence rate slows down in the later period, but it is constantly approaching the optimal solution. The faster initial convergence may be because the solution of the Dijkstra algorithm is poor. A better solution can be found after a simple crossover, mutation, and roulette algorithm. The genetic algorithm based on the greedy algorithm in the figure initially has a not-too-bad solution. Still, the initial crossover and mutation are not flexible, and to a certain extent, it is subject to the solution generated by the greedy algorithm. The medium term becomes faster because a better solution than the initial solution is generated, and the cross-matching roulette algorithm accelerates the convergence speed. As shown in Figures 5 and 6, as the size of the graph increases, the genetic algorithm based on the greedy algorithm will slow the convergence speed. In contrast, the genetic algorithm based on the Dijkstra algorithm still has a fast convergence speed in the early stage, while the convergence speed in the later stage has no obvious change. The reason for this phenomenon is that, as the size of the graph increases, the 'random' variation will make it more and more difficult to find a better solution. The improved Dijkstra algorithm, although in the early shrinkage limit to understand the space, has a very high crossover and mutation efficiency. Therefore, as the size of the graph increases, the genetic algorithm based on the Dijkstra algorithm will be more advantageous. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Network Topological Parameter** & **Task Parameter** & **Genetic Algorithm Parameters Based on Greedy Algorithm** & **Genetic Algorithm Based on Dijkstra Encoding** \\ \hline Station: 15 & Package: 200 & Mutation: 0.8 & Mutation: 0.8 \\ Cache: 30 & Deadline: 45 & Population: 100 & Population: 100 \\ \hline Station: 15 & Package: 250 & Mutation: 0.8 & Mutation: 0.8 \\ Cache: 30 & Deadline: 50 & Population: 100 & Population: 100 \\ \hline Station: 20 & Package: 100 & Mutation: 0.8 & Mutation: 0.8 \\ Cache: 40 & Deadline: 45 & Population: 100 & Population: 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Network Topological Parameter** & **Task Parameter** & **Genetic Algorithm Parameters Based on Greedy Algorithm** & **Genetic Algorithm Based on Dijkstra Encoding** \\ \hline Station: 10 & Package: 200 & Mutation: 0.8 & Mutation: 0.8 \\ Cache: 30 & Deadline: 45 & Population: 100 & Population: 100 \\ \hline Station: 10 & Package: 250 & Mutation: 0.8 & Mutation: 0.8 \\ Cache: 30 & Deadline: 45 & Population: 100 & Population: 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Convergence speed performance analysis parameter configuration. Figure 4: Comparison of fitness when Station is 10 and Package is 100. Figure 5: Comparison of fitness when Station is 15 and Package is 100. Figure 6: Comparison of fitness when Station is 20 and Package is 100. As shown in Figures 4 and 6, the number of data packets is increased, the genetic algorithm based on the greedy algorithm and the genetic algorithm based on the Dijkstra algorithm are compared, and the convergence speed does not change significantly.