With the explosive growth of multimedia contents in vehicular networks and the development of 5G technology come great challenges to the Internet of Vehicles system. By utilizing the storage capability of roadside units, popular contents can be cached in advance during nonpeak periods, which can bring better quality of experience to users. However, it is difficult for the existing content caching schemes in traditional vehicular networks to achieve global control when designing content caching mechanisms. Moreover, some content caching schemes also bring security concerns. Thus, in this article, we first discuss the content caching problem in the 5G-based cognitive Internet of Vehicles. Then we design security enhancement methods with the cognitive engine. Finally, we use a special case to study how to design a secure and delay-sensitive content caching scheme in cognitive vehicular networks. Extensive experiments show that the proposed algorithm outperforms traditional content caching methods.

中文翻译：

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基于5G的车辆认知互联网的增强安全性的内容缓存

随着车载网络中多媒体内容的爆炸性增长以及5G技术的发展，对车载互联网系统提出了巨大的挑战。通过利用路边单元的存储能力，可以在非高峰时段提前缓存流行内容，从而为用户带来更好的体验质量。但是，传统的车载网络中现有的内容缓存方案在设计内容缓存机制时很难实现全局控制。而且，某些内容缓存方案也带来了安全隐患。因此，在本文中，我们首先讨论基于5G的车辆认知互联网中的内容缓存问题。然后，我们利用认知引擎设计了增强安全性的方法。最后，我们使用一个特例研究如何在认知车辆网络中设计一种安全且对延迟敏感的内容缓存方案。大量实验表明，该算法优于传统的内容缓存方法。