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A density-aware probabilistic interest forwarding method for content-centric vehicular networks

机译：以内容为中心的车载网络的密度感知概率兴趣转发方法

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摘要

Vehicular networks, compared to other wireless networks, face particular challenges due to the rapidly changing topology and intermittent connections. By eliminating the need to establish and maintain an end-to-end connection, Content-Centric Network (CCN) model has recently become an appropriate solution to meet the challenging demands of vehicular network communications. In this kind of network, the basic method of forwarding interest packets is flooding. This approach will result in excessive redundancy, serious contention, and collision to which it is referred as the broadcast storm problem. In this article, a probabilistic strategy is proposed to alleviate the impact of the broadcast storm on interest forwarding in content-centric vehicular networks. In this density-aware approach, each vehicle dynamically computes the probabilities based on the number of existing neighbors. A local density approximation method is presented, which uses the information provided by the newly modified Pending Interest Table (PIT) entries. Moreover, some time-based techniques are employed to give priority to potential forwarders. The simulation results indicate that the proposed work outperforms the basic CCN. With on average 40% lower network load and 65% fewer interests compared to the basic CCN, it shows only an overall 6% decrease in reachability.
机译：与其他无线网络相比，车辆网络，由于拓扑和间歇连接迅速变化而面临特殊挑战。通过消除建立和维护端到端连接的需要，以内容为中心的网络（CCN）模型最近成为满足车辆网络通信的具有挑战性需求的适当解决方案。在这种网络中，转发兴趣数据包的基本方法是洪水。这种方法将导致过度冗余，严重的争用和碰撞，并将其称为广播风暴问题。在本文中，提出了一种概率战略，以减轻广播风暴在以内容为中心的车辆网络中的利息转发的影响。在这种密度感知方法中，每个车辆基于现有邻居的数量动态计算概率。提出了一种局部密度近似方法，它使用由新修改的待处理兴趣表（PIT）条目提供的信息。此外，采用了一些基于时间的技术来优先考虑潜在的转发器。仿真结果表明，所提出的工作优于基本CCN。与基本CCN相比，平均较低的网络负荷降低了65％的兴趣，它仅显示了可达性的总体下降了6％。

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