Reinforcement Learning Based Neighbour Selection for VANET with Adaptive Trust Management

Abstract

Successful information propagation from source to destination in Vehicular Adhoc Network (VANET) can be hampered by the presence of neighbouring attacker nodes causing unwanted packet dropping. Potential attackers change their behaviour over time and remain undetected due to the ad-hoc nature of VANET. Capturing the dynamic attacker behaviour and updating the corresponding neighbourhood information without compromising the quality of service requirements is an ongoing challenge. This work proposes a Reinforcement Learning (RL) based neighbour selection framework for VANET with an adaptive trust management system to capture the behavioural changes of potential attackers and to dynamically update the neighbourhood information. In contrast to existing works, we consider trust and link-life time in unison as neighbour selection criteria to achieve trustworthy communication. Our adaptive trust model takes into account the social relationship, time and confidence in trust observation to avoid four types of attackers. To update the neighbourhood information, our framework sets the learning rate of the RL agent according to the velocities of the neighbour nodes to improve the model's adaptability to network topology changes. Results demonstrate that our method can take less number of hops to the destination for large network sizes while can response is up to 54 percent faster compared to a baseline method. Also, the proposed model can outperform the other baseline method by reducing the packet dropping rate up to 57 percent caused by the attacker.