Nowadays, Wireless Vehicular Ad-Hoc Network (VANET) has become a valuable asset for transportation systems. However, this advanced technology is characterized by highly distributed and networked environment, which makes VANET communications vulnerable to malicious jamming attacks. Although Big Data Analytics has been used to solve this critical security issue by supporting the development of anti-jamming applications, as the amount of vehicular data is growing exponentially, the anti-jamming applications face many challenges (i., reactions in real-time) due to the lack of specific solutions that can keep up with the fast advancement of VANET. In this paper, we propose a new vehicular data prioritization model based on coresets to accelerate the Big Data Analytics in VANET. Our experimental evaluation shows that our solution can significantly increase the efficiency for clustering in jamming detection while keeping and improving the clustering quality. Also, the proposed solution can enable the real-time detection and be integrated to anti-jamming applications.

中文翻译：

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面向车载自组织网络中抗干扰应用的更快大数据分析

如今，无线车载自组织网络（VANET）已成为交通系统的宝贵资产。然而，这种先进技术的特点是高度分布式和网络化的环境，这使得 VANET 通信容易受到恶意干扰攻击。虽然大数据分析已经通过支持抗干扰应用的开发来解决这一关键的安全问题，但随着车载数据量呈指数级增长，抗干扰应用面临着许多挑战（即实时反应） ) 由于缺乏可以跟上 VANET 快速发展的特定解决方案。在本文中，我们提出了一种基于核心集的新车辆数据优先级模型，以加速 VANET 中的大数据分析。我们的实验评估表明，我们的解决方案可以显着提高干扰检测中的聚类效率，同时保持和提高聚类质量。此外，所提出的解决方案可以实现实时检测并集成到抗干扰应用程序中。