Biblio

Vehicular Ad Hoc Networks (VANETs) enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that bring many benefits and conveniences to improve the road safety and drive comfort in future transportation systems. Sybil attack is considered one of the most risky threats in VANETs since a Sybil attacker can generate multiple fake identities with false messages to severely impair the normal functions of safety-related applications. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. To avoid the inaccurate position estimation according to predefined radio propagation models in previous RSSI-based detection methods, Voiceprint adopts the RSSI time series as the vehicular speech and compares the similarity among all received time series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without the support of the centralized infrastructure. It has more accurate detection rate in different dynamic environments. Extensive simulations and real-world experiments demonstrate that the proposed Voiceprint is an effective method considering the cost, complexity and performance.

Vehicular ad hoc networks (VANETs) are designed to provide traffic safety by exploiting the inter-vehicular communications. Vehicles build awareness of traffic in their surroundings using information broadcast by other vehicles, such as speed, location and heading, to proactively avoid collisions. The effectiveness of these VANET traffic safety applications is particularly dependent on the accuracy of the location information advertised by each vehicle. Therefore, traffic safety can be compromised when Sybil attackers maliciously advertise false locations or other inaccurate GPS readings are sent. The most effective way to detect a Sybil attack or correct the noise in the GPS readings is localizing vehicles based on the physical features of their transmission signals. The current localization techniques either are designed for networks where the nodes are immobile or suffer from inaccuracy in high-interference environments. In this paper, we present a RSSI-based localization technique that uses mobile nodes for localizing another mobile node and adjusts itself based on the heterogeneous interference levels in the environment. We show via simulation that our localization mechanism is more accurate than the other mechanisms and more resistant to environments with high interference and mobility.