The new era of the Internet of Things is promoting the evolution of self-driving vehicles into connected and autonomous vehicles (CAVs). The deployment of CAVs in smart cities is highly dependent on the performance of their underlying networks known as vehicular networks. Designing an effective clustering approach is of great importance in such dynamic networks as it can significantly improve the reliability and scalability of the routing protocols. In this paper, we consider a heterogeneous vehicular network architecture that supports vehicle-to-vehicle and vehicle-to-infrastructure communications based on IEEE 802.11p and cellular networks (LTE/5G) with direct communications, namely cellular vehicle-to-everything (C-V2X) technologies. We introduce a novel clustering scheme for real-time routing based on the proposed network architecture. We formulate the problem of optimal clustering for connected and autonomous vehicles (OCCAV) and show that the problem is NP-hard. We then develop a clusters' stability maximization algorithm (CSM), which utilizes the stability degree of vehicles over a prediction horizon to efficiently solve the optimization problem in real-time. The algorithm is used within a rolling horizon framework for continuously solving the problem, making the resulting clusters adaptive to future traffic dynamics. We propose a hybrid routing protocol based on our clustering scheme, aiming to improve the packet delivery ratio and reduce the average delivery delay. For evaluation purposes, we use extensive realistic simulations based on mobility scenarios validated using real vehicular trajectories. The results demonstrate that our clustering scheme improves the alternative algorithms in terms of the average cluster head duration, cluster head change rate, cluster member duration, and overall stability by 61%, 62%, 44%, and 52%, respectively. CSM also outperforms clustering overhead by 54%. Compared to the other cluster-based routing, our scheme also achieves a higher packet delivery ratio by up to 30%, and a lower average delay by up to 52%. To provide an in-depth analysis of the optimality of our scheme and its alternatives, we also use the Gurobi optimizer to find an optimal solution to the OCCAV problem. The results suggest that our scheme can achieve near-optimal cluster stability.

中文翻译：

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异构车辆网络中增强路由的集群稳定性驱动优化


物联网新时代正在推动自动驾驶汽车向互联自动驾驶汽车（CAV）的演变。 CAV 在智慧城市中的部署高度依赖于其底层网络（称为车辆网络）的性能。设计有效的聚类方法在这种动态网络中非常重要，因为它可以显着提高路由协议的可靠性和可扩展性。在本文中，我们考虑一种异构车辆网络架构，支持基于 IEEE 802.11p 和蜂窝网络 (LTE/5G) 的车辆到车辆和车辆到基础设施通信，并进行直接通信，即蜂窝车辆到一切（ C-V2X）技术。我们基于所提出的网络架构引入了一种新颖的实时路由聚类方案。我们提出了互联自动驾驶车辆的最优聚类问题 (OCCAV)，并证明该问题是 NP 难问题。然后，我们开发了集群稳定性最大化算法（CSM），该算法利用车辆在预测范围内的稳定性程度来有效地实时解决优化问题。该算法在滚动范围框架内使用，持续解决问题，使生成的集群适应未来的交通动态。我们提出了一种基于集群方案的混合路由协议，旨在提高数据包传递率并减少平均传递延迟。出于评估目的，我们使用基于使用真实车辆轨迹验证的移动场景的广泛现实模拟。 结果表明，我们的聚类方案在平均簇头持续时间、簇头变化率、簇成员持续时间和整体稳定性方面分别将替代算法提高了 61%、62%、44% 和 52%。 CSM 的性能也比集群开销高出 54%。与其他基于集群的路由相比，我们的方案还实现了高达 30% 的更高的数据包投递率，以及高达 52% 的更低的平均延迟。为了深入分析我们的方案及其替代方案的最优性，我们还使用 Gurobi 优化器来寻找 OCCAV 问题的最优解决方案。结果表明我们的方案可以实现接近最优的集群稳定性。