Resilience-oriented communication topology optimization for airport snow removal IoV networks
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摘要: 针对机场除雪集群作业中因通信节点故障导致协同控制失效及作业效能下降的问题，本文提出了一种基于车联网拓扑结构优化与动态队形重构的韧性提升方法。首先，剖析机场除雪作业的动态交互特征，定义了面向任务过程的性能评价指标，并据此构建了除雪集群作业韧性评估模型；其次，为增强通信网络在受损工况下的抗毁性，引入节点度方差作为结构生存性测度指标；在此基础上，建立了车联网拓扑优化的混合整数半正定规划模型，并利用凸松弛技术将该NP难问题分解为基于整数二次规划的度矩阵优化与基于半正定规划的图可行解问题，实现了复杂约束下的高效求解；进一步，针对节点失效后的覆盖断点问题，设计基于重映射的动态紧缩重构策略，通过逻辑拓扑的快速自愈实现物理作业间隙的及时闭合。仿真试验结果表明：所提方法显著提升了系统的故障容错能力；在6节点随机网络试验中，优化后的拓扑结构在50%节点失效的极端工况下仍能维持基本的编队构型功能，系统作业韧性较优化前提升了13.1%；进一步的蒙特卡洛统计与参数敏感性分析表明，节点度方差能够在统计意义上反映网络连通保持能力，车辆横向间距与最小重叠宽度对有效覆盖宽度具有明显影响。研究验证了拓扑优化设计有助于提高机场网联车辆在恶劣环境下的作业韧性。Abstract: In response to the collaborative control failure and operational efficiency degradation caused by communication node failures in airport snow removal cluster operations, a resilience enhancement method was proposed based on the Internet of Vehicles (IoV) topology optimization and dynamic formation reconfiguration. First, the dynamic interaction characteristics of snow removal operations were analyzed. A mission process-oriented performance evaluation metric was defined. Based on this, a resilience assessment model was developed for the snow removal cluster operations. Second, to enhance the invulnerability of the communication network under damaged conditions, the node degree variance was introduced as a structural survivability index. On this basis, a mixed integer semi-definite programming model for IoV topology optimization was formulated. Convex relaxation techniques were applied to decompose this NP-hard problem into a degree matrix optimization problem based on integer quadratic programming and a graph feasible solution problem based on semi-definite programming, thus achieving efficient solutions under complex constraints. Furthermore, to address the issue of coverage gaps caused by node failures, a dynamic contractive reconfiguration strategy based on remapping was designed to achieve the timely closure of physical operational gaps through the rapid self-healing of the logical topology. Simulation results demonstrate that the proposed method significantly improves the system's fault tolerance. In a 6-node stochastic network experiment, the optimized topological structure maintains basic formation configuration functions even under an extreme condition of 50% node loss, with the operational resilience improved by 13.1% after optimization. Further Monte Carlo statistics and parameter sensitivity analysis indicate that the node degree variance can reflect the network connectivity retention ability in a statistical sense. The vehicle lateral spacing and minimum overlap width have a significant impact on the effective coverage width. The effectiveness of topology optimization design in enhancing the operational resilience of connected airport vehicles under harsh environmental conditions is validated.
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图 4 机场除雪作业集群车联网-物理层双层架构
Figure 4. Airport snowplow fleets vehicle networking-physical layer dual layer architecture
图 7 基于集中式重构方法下的除雪车作业集群运动轨迹
Figure 7. Trajectory evolution of snow removal vehicle fleet under centralized reconfiguration
图 12 随机通信拓扑下3次节点故障条件下的运动轨迹和作业性能演化曲线
Figure 12. Trajectories and performance evolution curves under random communication topology with three node failures
图 13 优化通信拓扑下3次节点故障条件下的运动轨迹和作业性能演化曲线
Figure 13. Trajectories and performance evolution curves under optimized communication topology with three node failures
图 14 环形拓扑下3次节点故障条件下的运动轨迹和作业性能演化曲线
Figure 14. Trajectories and performance evolution curves under ring topology with three node failures
图 15 优化通信拓扑下4次节点故障条件下的运动轨迹和作业性能演化曲线
Figure 15. Trajectories and performance evolution curves under optimized communication topology with four node failures
图 16 全连通拓扑下4次节点故障条件下的运动轨迹和作业性能演化曲线
Figure 16. Trajectories and performance evolution curves under fully connected topology with four node failures
图 17 节点度方差H1与代数连通度λ2的初始关联关系
Figure 17. Initial correlation between node degree variance H1 and algebraic connectivity λ2
图 18 不同故障次数下H1与故障后平均代数连通度的关系
Figure 18. Relationship between H1 and post-failure average algebraic connectivity under different failure numbers
图 19 不同故障次数下H1与连通保持概率的关系
Figure 19. Relationship between H1 and connectivity preservation probability under different failure numbers
图 20 车辆横向间距与最小重叠宽度对有效覆盖宽度影响热力图
Figure 20. Heatmap of the influence of vehicle lateral spacing and minimum overlap width on effective coverage width
表 1 不同场景下的韧性表现
Table 1. Resilience performance in different scenarios
场景 1 2 3 韧性指标 0.768 0.355 0.721 
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表 2 不同拓扑下的韧性表现
Table 2. Resilience performances in different scenarios
拓扑结构 随机拓扑 优化拓扑 环形拓扑 拓扑结构 随机拓扑 优化拓扑 环形拓扑
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