Local Voting Games for Misbehavior Detection in VANETs in Presence of Uncertainty

TL;DR

This paper introduces a game-theoretic model using Bayesian voting to improve misbehavior detection in VANETs, accounting for uncertainty and incentives, with analysis and simulations demonstrating its effectiveness.

Contribution

It proposes a novel Bayesian game-based voting scheme for VANETs that incorporates uncertainty and incentives to enhance malicious node detection.

Findings

Uncertainty and incentives significantly influence node strategies.

The equilibrium analysis ensures stable cooperation among nodes.

Simulations confirm improved detection accuracy under the proposed model.

Abstract

Cooperation between neighboring vehicles is an effective solution to the problem of malicious node identification in vehicular ad hoc networks (VANETs). However, the outcome is subject to nodes' beliefs and reactions in the collaboration. In this paper, a plain game-theoretic approach that captures the uncertainty of nodes about their monitoring systems, the type of their neighboring nodes, and the outcome of the cooperation is proposed. In particular, one stage of a local voting-based scheme for identifying a target node is developed using a Bayesian game. In this context, incentives are offered in expected utilities of nodes in order to promote cooperation in the network. The proposed model is then analyzed to obtain equilibrium points, ensuring that no node can improve its utility by changing its strategy. Finally, the behavior of malicious and benign nodes is studied by extensive simulation results. Specifically, it is shown how the existing uncertainties and the designed incentives impact the strategies of the players and, consequently, the correct target-node identification.