Abstract:
A well-designed vehicular ad hoc network (VANET) is essential to building a smart transportation system that supports traffic safety and high-efficiency transportation. VANETs’ dynamicity and infrastructure-less nature make them vulnerable to malicious nodes that degrade performance. SDN allows dynamic VANET management. In this article, we propose a novel software-defined trust-based VANET architecture (SD-TDQL) in which the centralized SDN controller acts as a learning agent to find the optimal communication link policy using deep Q-learning. In a Markov decision process with state space, action space, and reward function, a joint optimization problem considers vehicle trust and reverse delivery ratio. We evaluate connected vehicle communication link quality using the expected transmission count (ETX). We also create a trust model to avoid malicious vehicles. SD-TDQL improves link quality in simulations.
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