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.

与其他无线网络相比，车载网络由于拓扑结构的快速变化和间歇性连接而面临着特殊的挑战。通过消除建立和维护端到端连接的需求，内容中心网络（CCN）模型最近已成为满足车载网络通信挑战性要求的合适解决方案。在这种网络中，转发兴趣数据包的基本方法是泛洪。这种方法将导致过多的冗余，严重的争用和冲突，将其称为广播风暴问题。在本文中，提出了一种概率策略来减轻广播风暴对以内容为中心的车载网络中的兴趣转发的影响。在这种密度感知方法中，每辆车都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，该方法使用了新修改的“未付利息表”（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货运代理。仿真结果表明，所提出的工作优于基本的CCN。与基本CCN相比，平均网络负载降低了40％，兴趣减少了65％，可访问性仅下降了6％。