Towards faster big data analytics for anti-jamming applications in vehicular ad-hoc network

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.