Vehicular ad hoc networks (VANETs) have become a promising technology in intelligent transportation systems (ITS) with rising interest of expedient, safe, and high-efficient transportation. VANETs are vulnerable to malicious nodes and result in performance degradation because of dynamicity and infrastructure-less. In this paper, we propose a trust based dueling deep reinforcement learning approach (T-DDRL) for communication of connected vehicles, we deploy a dueling network architecture into a logically centralized controller of software-defined networking (SDN). Specifically, the SDN controller is used as an agent to learn the most trusted routing path by deep neural network (DNN) in VANETs, where the trust model is designed to evaluate neighbors’ behaviour of forwarding routing information. Simulation results are presented to show the effectiveness of the proposed T-DDRL framework.

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8th ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2018, in conjunction with the 21st ACM International Conference on Modeling, Analysis and Simulations of Wireless and Mobile Systems, MSWiM 2018
School of Information Technology

Zhang, D. (Dajun), Yu, F.R, Yang, R. (Ruizhe), & Tang, H. (Helen). (2018). A deep reinforcement learning-based trust management scheme for software-defined vehicular networks. In DIVANet 2018 - Proceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (pp. 1–7). doi:10.1145/3272036.3272037