A Machine Learning Approach for Software-Defined Vehicular Ad Hoc Networks with Trust Management

Vehicular ad hoc networks (VANETs) have become a promising technology in smart transportation systems with rising interest of expedient, safe, and high- efficient transportation. Dynamicity and infrastructure-less of VANETs make it vulnerable to malicious nodes and result in performance degradation. In this paper, we propose a software- defined trust based deep reinforcement learning framework (TDRL-RP), deploying a deep Q-learning algorithm into a logically centralized controller of software-defined networking (SDN). Specifically, the SDN controller is used as an agent to learn the highest routing path trust value of a VANET environment by convolution neural network, where the trust model is designed to evaluate neighbors' behaviour of forwarding packets. Simulation results are presented to show the effectiveness of the proposed TDRL-RP framework.