Distributed Misbehavior Detection in VANETs

In any vehicular adhoc network, there is always a possibility of incorrect messages being transmitted either due to faulty sensors and/or intentional malicious activities. Detecting and evicting sources of such misbehavior is an important problem. We observe that the performance of misbehavior detection schemes will depend on the application under consideration and the mobility dynamics of the detecting vehicle. Further, the underlying tradeoff in any such detection algorithm is the balance between False Positives and False Negatives; one would like to detect as many misbehaviors as possible, while at the same time ensuring that the genuine vehicles are not wrongly accused. In this work we propose and analyze (via simulations) the performance of a Misbehavior Detection Scheme (MDS) for Post Crash Notification (PCN) application. We observe that the performance of this proposed scheme is not very sensitive to the exact dynamics of the vehicle on small scales, so that slight error in estimating the dynamics of the detecting vehicle does not degrade the performance of the MDS.