On Reputation and Data-centric Misbehavior Detection Mechanisms for VANET

Abstract

Vehicular ad hoc networks (VANET) is a class of ad hoc networks build to ensure the safety of traffic. This is important because accidents claim many lives. Trust and security remain a major concern in VANET since a simple mistake can have catastrophic consequence. A crucial point in VANET is how to trust the information transmitted when the neighboring vehicles are rapidly changing and moving in and out of range. Current reputation systems for VANET try to establish trust between entities, which might not be required for practical scenarios. Due to the ephemeral nature of VANET, reputation schemes for mobile ad hoc networks (MANETs) cannot be applied to VANET. In this thesis, we point out several limitations of reputation trust management schemes for VANET. In particular, we identify the problem of information cascading and oversampling, which commonly arise in social networks. Oversampling is a situation in which a node observing two or more nodes, takes into consideration both their opinions equally without knowing that they might have influenced each other in decision making. We show that simple voting for decision making, leads to oversampling. We propose a solution to overcome this problem in VANET. We also suggest new ways to merge reputation schemes with misbehavior detection schemes to establish a trustworthy VANET.