  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.
