Deep Reinforcement Learning-Based Intelligent Security Forwarding Strategy for VANET

Abstract

The vehicular ad hoc network (VANET) constitutes a key technology for realizing intelligent transportation services. However, VANET is characterized by diverse message types, complex security attributes of communication nodes, and rapid network topology changes. In this case, how to ensure safe, efficient, convenient, and comfortable message services for users has become a challenge that should not be ignored. To improve the flexibility of routing matching multiple message types in VANET, this paper proposes a secure intelligent message forwarding strategy based on deep reinforcement learning (DRL). The key supporting elements of the model in the strategy are reasonably designed in combination with the scenario, and sufficient training of the model is carried out by deep Q networks (DQN). In the strategy, the state space is composed of the distance between candidate and destination nodes, the security attribute of candidate nodes and the type of message to be sent. The node can adaptively select the routing scheme according to the complex state space. Simulation and analysis show that the proposed strategy has the advantages of fast convergence, well generalization ability, high transmission security, and low network delay. The strategy has flexible and rich service patterns and provides flexible security for VANET message services.