RLID-V: Reinforcement Learning-Based Information Dissemination Policy Generation in VANETs

Ciphertext policy attribute-based encryption (CP-ABE) is popularly used to implement secure and accurate access control of disseminated information in vehicular ad hoc networks (VANETs). Nevertheless, how to improve the policy generation of CP-ABE for accurate information dissemination in the dynamic VANETs remains a challenge, as there are several access control policies rising from moving vehicles and road side units (RSUs) with different sensing boarder regarding to a specific event, such as moving vehicles and road side units (RSUs). To solve this problem, this paper proposes a reinforcement learning-based information dissemination policy generation scheme in VANETs, named RLID-V. The scheme firstly combines multiple attribute-based access control policies and resolves policy conflicts between vehicles and RSUs. Then, a manual feedback policy construction method is designed by applying decision tree to the collected feedback from all receivers. Finally, we employ reinforcement learning to dynamically update the confidence weights of different policy sources. The experiments are conducted in two classic VANETs scenarios, traffic guidance and accident warning, demonstrating that RLID-V achieves better performance in the accuracy and effectiveness of information dissemination compared with three existing schemes. Otherwise, RLID-V outperforms the compared schemes in robustness with 20% error feedback and takes a negligible cost of less than 1% of the overall delay overhead for policy generation.