V2X Routing in a VANET Based on the Hidden Markov Model
V2X Routing in a VANET Based on the Hidden Markov Model
复制标题
VANET中基于隐马尔可夫模型的V2X路由
DOI：
10.1109/tits.2017.2706756
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发表时间：
2018-03-01
影响因子：
8.5
通讯作者：
Wang, Yuqi
中科院分区：
文献类型：
--
作者：
Yao, Lin;Wang, Jie;Wang, Yuqi
由于车辆速度快、车距长、车辆密度变化大，在车辆自组网中建立和维护端到端连接非常困难。相反，存储转发策略已被考虑用于车辆通信。然而，这种策略的成功在很大程度上取决于节点之间的合作。不同于现有的存储转发解决方案，我们提出了基于隐马尔可夫模型（PRHMM）的预测路由VANETS，它利用车辆移动行为的规律性，以提高传输性能。由于车辆运动经常表现出高度的重复性，包括定期访问某些地方和日常活动中的定期接触，我们可以根据过去的轨迹和隐马尔可夫模型的知识来预测车辆的未来位置。因此，可以预测车辆的短期路线及其针对特定移动的目的地的分组递送概率。此外，PRHMM实现了车辆到车辆和车辆到基础设施通信之间的无缝切换，使得传输性能不会受到车辆密度和移动速度的限制。仿真结果表明，PRHMM表现出更好的交付率，端到端延迟，流量开销，和缓冲区占用。
It is very difficult to establish and maintain end-to-end connections in a vehicle ad hoc network (VANET) as a result of high vehicle speed, long inter-vehicle distance, and varying vehicle density. Instead, a store-and-forward strategy has been considered for vehicle communications. The success of this strategy, however, depends heavily on the cooperation among nodes. Different from exiting store-and-forward solutions, we propose predictive routing based on the hidden Markov model (PRHMM) for VANETS, which exploits the regularity of vehicle moving behaviors to increase the transmission performance. As vehicle movements often exhibit a high degree of repetition, including regular visits to certain places and regular contacts during daily activities, we can predict a vehicle's future locations based on the knowledge of past traces and the hidden Markov model. Consequently, the short-term route of a vehicle and its packet delivery probability for a specific mobile destination can be predicted. Moreover, PRHMM enables seamless handoff between vehicle-to-vehicle and vehicle-to-infrastructure communications so that the transmission performance will not be constrained by the vehicle density and moving speed. Simulation evaluation demonstrates that PRHMM performs much better in terms of delivery ratio, end-to-end delay, traffic overhead, and buffer occupancy.