## 4 Simulation Results
### Packet Loss Rate Results  
The proposed algorithm utilizes CHCF and the improved ACO algorithm to elect cluster heads and find the best route, and is compared with the LEACH and AODV algorithms. The process was set so that each node had 1000 packets to be sent every round, and the network ran 500 rounds. The statistical packet loss rate results are shown in Figure 3.  
Figure 3a shows that the packet loss rate obtained by the proposed algorithm is significantly lower than the packet loss rate obtained by LEACH and AODV, and the error bars of the average packet loss rate are shown in Figure 3b. In Figure 3b, the bars represent the mean value of packet loss rate from network round 1 to round 500, and the vertical line at the top of the bars represents the standard deviation of packet loss rate from network round 1 to round 500. The mean values of packet loss rate are 0.25% and 22.37% for the proposed algorithm and comparison algorithm, respectively. It can be seen from Figure 3b that the proposed algorithm has a smaller and more stable packet loss rate than LEACH and AODV. The reason is that the proposed algorithm comprehensively considers multiple factors to elect cluster heads each round. However, LEACH only elect cluster heads according to a random number in each round, and the node which has been the cluster head cannot continue to serve as the cluster head in the next round. Therefore, the cluster head elected by LEACH can only guarantee the minimum packet loss rate for a short time. In addition, LEACH may elect the nodes at the edge positions to be cluster heads, which  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Parameter**} & \multicolumn{1}{c}{**Value**} \\ \hline  & Carrier frequency & 2.4 GHz \\  & Data rate & 20 Mbps \\ Parameters of vehicle–ground & Transmit power & 10 dBm \\ communication & Tx Antenna gain & 5 dBi \\  & Rx Antenna gain & 5 dBi \\  & Receiver sensitivity & \(-77\) dBm \\ \hline  & \(\alpha\), \(\beta\) & 1, 10 \\ Parameters of the improved & \(\alpha_{1},\alpha_{2},\alpha_{3}\) & 0.5, 0.3, 0.2 \\ ACO algorithm & \(Q\) & 1 \\  & \(\rho(0)\) & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
leads to the result that the packet loss rate fluctuates widely in different rounds. In contrast, the proposed algorithm re-elects the cluster heads according to the packet loss rate during each round. Thus, a node with a packet loss rate higher than \(c_{threshold}\) cannot be the cluster head in the next round. Therefore, the packet loss rate obtained by the proposed algorithm only fluctuates in a small range and always keeps a low value.  
Considering the train is moving and the surrounding environment is constantly changing, it is necessary to validate the performance of the proposed algorithm under circumstances of different channel quality. In Figure 4, we depict the average packet loss when the train is at different positions and obtain the results for different channel qualities. The figure shows that the packet loss rate has an increasing trend, which is because the data packets are sent to the previous station as the train moves on. As the channel quality changes from good to bad, the received signal power decreases, and the packet loss rates of the proposed algorithm and the comparison algorithm both increase. However, the comparison algorithm is affected by channel quality and driving distance more seriously than the proposed algorithm. When the channel quality is bad, the maximum packet loss rate exceeds 20% for LEACH and AODV, while that for the proposed algorithm can still be below 10%. The reason is that as the train moves away from the station, the improved ACO algorithm can always find the best way depending on the shortest distance to neighboring nodes, routing direction, and node loads.  
Figure 4: The average packet loss rate at different positions.  
Figure 3: The statistical packet loss rate: (**a**) Packet loss rate of each round; (**b**) The error bars of the mean value of packet loss rate.