Improving Big Data Clustering for Jamming Detection in Smart Mobility

Abstract

Smart mobility, with its urban transportation services ranging from real-time traffic control to cooperative vehicle infrastructure systems, is becoming increasingly critical in smart cities. These smart mobility services thus need to be very well protected against a variety of security threats, such as intrusion, jamming, and Sybil attacks. One of the frequently cited attacks in smart mobility is the jamming attack. In order to detect the jamming attacks, different anti-jamming applications have been developed to reduce the impact of malicious jamming attacks. One important step in anti-jamming detection is to cluster the vehicular data. However, it is usually very time-consuming to detect the jamming attacks that may affect the safety of roads and vehicle communication in real-time. Therefore, this paper proposes an efficient big data clustering model, coresets-based clustering, to support the real-time detection of jamming attacks. We validate the model efficiency and applicability in the context of a typical smart mobility system: Vehicular Ad-hoc Network, known as VANET.

The role of digital technologies to unleash a green recovery: pathways and pitfalls to achieve the European Green Deal

“…, 2021; Haftor and Climent, 2021; Qi and Shen, 2019). BDA can play a key role in supporting the real-time detection of security threats like jamming and intrusion, which are posing security threats in urban transportation services in smart cities (Bangui and Buhnova, 2020).…”

“…These applications have the potential to provide benefits like real-time information, predictive maintenance, intelligent parking and traffic management, automated toll collection, improved surveillance, road safety and monitoring pollution levels (Zenkert et al, 2018;Di Vaio et al, 2020;Kristoffersen et al, 2020;Marsal-Llacuna, 2020;de Villiers et al, 2021;Haftor and Climent, 2021;Qi and Shen, 2019). BDA can play a key role in supporting the realtime detection of security threats like jamming and intrusion, which are posing security threats in urban transportation services in smart cities (Bangui and Buhnova, 2020).…”

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

“…In order to solve the efficiency issues in Big Data Analytics for anti-jamming applications, 14,15 this paper proposes a solution for jamming detection that can handle vehicular data in a real-time setting. The coresets method is used to extract a small set that effectively represents the original data and then solves the compression issues of trajectory data in road networks.…”