School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Traffic Control Technology Co., Ltd., Beijing 100071, China.
Sensors (Basel). 2023 Jun 14;23(12):5576. doi: 10.3390/s23125576.
With the advancement of urban rail transit towards intelligence, the demand for urban rail transit communication has increased significantly, but the traditional urban rail transit vehicle-ground communication system has been unable to meet the future vehicle-ground communication requirements. To improve the performance of vehicle-ground communication, the paper proposes a reliable low-latency multipath routing (RLLMR) algorithm for urban rail transit ad hoc networks. First, RLLMR combines the characteristics of urban rail transit ad hoc networks and uses node location information to configure a proactive multipath to reduce route discovery delay. Second, the number of transmission paths is adaptively adjusted according to the quality of service (QoS) requirements for vehicle-ground communication, and then the optimal path is selected based on the link cost function to improve transmission quality. Third, in order to enhance the reliability of communication, a routing maintenance scheme has been added, and the static node-based local repair scheme is used in routing maintenance to reduce the maintenance cost and time. The simulation results show that compared with traditional AODV and AOMDV protocols, the proposed RLLMR algorithm has good performance in improving latency and is slightly inferior to the AOMDV protocol in improving reliability. However, overall, the throughput of the RLLMR algorithm is better than that of the AOMDV.
随着城市轨道交通向智能化的发展,城市轨道交通通信的需求显著增加,但传统的城市轨道交通车地通信系统已经无法满足未来车地通信的要求。为了提高车地通信的性能,本文针对城市轨道交通自组织网络提出了一种可靠的低延迟多径路由(RLLMR)算法。首先,RLLMR 结合了城市轨道交通自组织网络的特点,利用节点位置信息配置主动多径,以减少路由发现延迟。其次,根据车地通信的服务质量(QoS)要求自适应调整传输路径的数量,然后根据链路代价函数选择最优路径,以提高传输质量。第三,为了增强通信的可靠性,增加了路由维护方案,在路由维护中采用基于静态节点的局部修复方案,以降低维护成本和时间。仿真结果表明,与传统的 AODV 和 AOMDV 协议相比,所提出的 RLLMR 算法在降低延迟方面具有良好的性能,在提高可靠性方面略逊于 AOMDV 协议。但总体而言,RLLMR 算法的吞吐量优于 AOMDV。