Software-Defined Vehicular Networks With Trust Management: A Deep Reinforcement Learning Approach

The appropriate design of a vehicular ad hoc network (VANET) has become a pivotal way to build an efficient smart transportation system, which enables various applications associated with traffic safety and highly-efficient transportation. VANETs are vulnerable to the threat of malicious nodes stemming from its dynamicity and infrastructure-less nature and causing performance degradation. Recently, software-defined networking (SDN) has provided a feasible way to manage VANETs dynamically. In this article, we propose a novel software-defined trust based VANET architecture (SD-TDQL) in which the centralized SDN controller is served as a learning agent to get the optimal communication link policy using a deep $Q$ -learning approach. The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Specifically, we use the expected transmission count ( $ETX$ ) as a metric to evaluate the quality of the communication link for the connected vehicles’ communication. Moreover, we design a trust model to avoid the bad influence of malicious vehicles. Simulation results prove that the proposed SD-TDQL framework enhances the link quality.