  Vehicular Ad hoc Networks (VANETs) facilitate vehicles to wirelessly
communicate with neighboring vehicles as well as with roadside units (RSUs).
However, the existence of inaccurate information within the network can cause
traffic aberrations and also disrupt the normal functioning of any traffic
monitoring system. Thus, determining the credibility of broadcast messages
originating from the region of interest (ROI) is crucial under a malicious
environment. Additionally, a breach of privacy involving a vehicle's private
information, such as location and velocity, can lead to severe consequences
like unauthorized tracking and masquerading attack. 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 such as congestion. 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
peer-based authentication model, the majority voting model, and the
reputation-based system under different attack scenarios. We show that our
model is capable of filtering malicious vehicles effectively and provide
accurate traffic information under the presence of at least one non-malicious
vehicle within the ROI.
