Green Vehicular Ad-hoc Network (VANET) is a newly-emerged research area which focuses on reducing harmful impacts of vehicular communication equipments on the natural environment. Recent studies have shown that grouping vehicles into clusters for green communications in VANETs can significantly improve networking efficiency and reduce infrastructure costs. As a dynamic network system, maintaining the network connectivity and reducing the communication overlap are two critical challenges for green VANET clustering. However, most existing work studies connectivity and overlap separately, lacking a deep understanding of the relationship between them. To address this issue, we present a comprehensive analysis that jointly considers the two critical factors in one model. Specifically, we first design a state resemblance prediction (SRP) model based on the historical trajectory feature relevance between vehicles; Combined with the SRP model, we propose the region-based collaborative management scheme (RCMS) to establish the dynamic clustering; Lastly, we take extensive experiments to verify the region-based collaborative management scheme for dynamic clustering. The results demonstrate that the proposed clustering algorithm can achieve high networking efficiency and better communication stability.
翻译:最新研究表明,将车辆分组为VANET的绿色通信集群可以大大提高联网效率并降低基础设施成本。作为一个动态网络系统,维护网络连通性和减少通信重叠是绿色VANET集群的两大重大挑战。然而,大多数现有工作研究的连通性和重叠是单独出现的,对二者之间的关系缺乏深刻了解。为解决这一问题,我们提交了一份综合分析,将两种关键因素放在一个模型中共同考虑。具体地说,我们首先根据车辆之间的历史轨迹相关性设计了状态相似性预测模型;与SRP模型相结合,我们提出了建立动态集群的基于区域的协作管理计划;最后,我们进行了广泛的实验,以核实动态集群的基于区域的协作管理计划。结果表明,拟议的组合算法可以实现高联网效率和更好的通信稳定性。