Secure and Privacy-preserving Traffic Monitoring in VANETs

Abstract

Vehicular Ad hoc Networks (VANETs) facilitate vehicles to wirelessly communicate with neighboring vehicles as well as with roadside units (RSUs). However, an attacker can inject inaccurate information within the network that can cause various security and privacy threats, and also disrupt the normal functioning of any traffic monitoring system. Thus, we propose an edge cloud-based privacy-preserving secured decision making model that employs a heuristic based on vehicular data such as GPS location and velocity to authenticate traffic-related information from the ROI under different traffic scenarios. The effectiveness of the proposed model has been validated using VENTOS, SUMO, and Omnet++ simulators, and also, by using a simulated cloud environment. We compare our proposed model to the existing state-of-the-art models under different attack scenarios. We show that our model is effective and capable of filtering data from malicious vehicles, and provide accurate traffic information under the influence of at least one non-malicious vehicle.