# A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks
## 3 The Improved Multipath Routing Algorithm
### RLLMR Algorithm
This process only occurs when needed, saving link overhead. However, due to multi hop transmission, the route discovery time is long. The RLLMR algorithm configures the routing information in the trackside node before the run begins, allowing trains to obtain destination node routing information from wayside nodes, greatly reducing the latency of route discovery. However, this method requires a certain amount of link overhead, so the link overhead will be greater than that of AODV and AOMDV. Second, in the routing process, both AODV and AOMDV choose paths based on the minimum number of hops, but AODV only selects one path for data transmission, which is easily affected by network congestion, resulting in a high end-to-end time delay and decreased packet delivery rate. AOMDV reduces the link load and reduces the impact of network congestion by using multiple paths for transmission, thereby improving transmission efficiency. In addition, AOMDV also reserves backup paths, which can be used for retransmission when data packets are lost, improving the packet delivery rate. The RLLMR algorithm draws inspiration from AOMDV but differs from AOMDV in that it selects different numbers of paths for transmission based on different latency requirements of services and takes into account two factors: hop count and link quality. By selecting paths with a small hop count and good link quality, the packet delivery rate is improved. In fact, the more transmission paths there are, the greater the transmission efficiency is. However, AOMDV uses the same number of paths for both low-latency and high-latency services, and it may waste path resources or may not meet the latency requirements of the services. Therefore, the RLLMR adaptive path selection method can effectively meet the latency requirements of different services. Third, AODV does not have a repair plan for packet loss, resulting in a high packet loss rate. As mentioned earlier, AOMDV will retransmit through an alternate path, but enabling an alternate path will result in data needing to be resent from the source node, which takes a long time. In RLLMR, the maintenance node is used to directly retransmit data packets at the current node without returning to the source node. Therefore, this method shortens the routing repair time. However, the repair solution in RLLMR reduces the packet delivery rate in harsh network environments due to the congestion of maintenance nodes due to the need for too many nodes to rely on maintenance nodes. Although RLLMR has a lower packet loss rate than AOMDV in harsh environments, its routing repair time is faster than that of AOMDV. In addition, the throughput is related to the packet loss rate and latency, and the results indicate that the throughput of RLLMR is higher than that of AOMDV. Overall, the performance of RLLMR is superior to that of AOMDV and AODV. In fact, the RLLMR method proposed in this paper has many limitations. First, this method is only applicable in network scenarios where there are a large number of static nodes and the destination node location is clear. If there are a large number of dynamic nodes, static configuration strategies cannot be implemented, resulting in an increase in routing discovery latency. Second, this method is only applicable when the mobile node prioritizes sending requests to the ground node to establish a wireless communication link and cannot be prioritized by the ground node to send requests to the mobile node because the train is moving and the ground cannot obtain the train's position information first. Finally, this method currently only considers the situation where a monorail train is heading towards a station, without worrying about energy distribution. ## 6 Conclusions

In this paper, an RLLMR algorithm is proposed to address the problem of traditional on-demand ad hoc routing to meet low-latency and high-reliability vehicle-ground communication requirements. The RLLMR contains static configuration, the AMS algorithm, and the LRM scheme.