Cooperative Downloading in Vehicular Networks: A Graph-Based Approach
Cooperative Downloading in Vehicular Networks: A Graph-Based Approach
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DOI：
10.1109/vtcspring.2018.8417806
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Yanglong Sun;Le Xu;Yuliang Tang
中科院分区：
文献类型：
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作者：
Yanglong Sun;Le Xu;Yuliang Tang
虽然家庭用户可以很容易地将各种内容检索到他们的笔记本电脑或智能手机上，但车载用户受到与路边设备(RSU)断断续续连接的限制。本文提出了一种同构车载网络中的协同下载机制。在这种机制中，RSU充当交通管理员，从互联网获取适当的数据，然后以接近最佳的方式分发给车辆。具体地，基于车辆机动性预测和节点间吞吐量估计，构建存储时间聚合图(STAG)用于规划传输方案，然后设计一种迭代贪婪驱动算法来获得次优解。仿真结果表明，在RSU间距不大于1500m的均匀分布式部署场景中，该方法可以减少5%-20%的∼下载时间。
While home users can easily retrieve all kinds of contents onto their laptops or smartphones, vehicular users are constrained by intermittent connectivity to roadside units (RSUs). In this paper, we propose a cooperative downloading mechanism in homogeneous vehicular networks. In this mechanism, RSUs act as traffic managers to fetch proper data from the Internet and then distribute to vehicles in an approximately optimal manner. Specifically, based on vehicular mobility prediction and inter-node throughput estimation, a storage time aggregated graph (STAG) is constructed for planning transmission scheme, then an iterative greedy-driven algorithm is designed for deriving a suboptimal solution. Simulation results show that our approach can reduce 5% ∼ 20% downloading time in an uniform distributed deployment scenario where the spacing between RSUs is not greater than 1500m.