A fuzzy reward and punishment scheme for vehicular ad hoc networks
<|reference_start|>A fuzzy reward and punishment scheme for vehicular ad hoc networks: Trust management is an important security approach for the successful implementation of Vehicular Ad Hoc Networks (VANETs). Trust models evaluate messages to assign reward or punishment. This can be used to influence a driver's future behaviour. In the author's previous work, a sender side based trust management framework is developed which avoids the receiver evaluation of messages. However, this does not guarantee that a trusted driver will not lie. These "untrue attacks" are resolved by the RSUs using collaboration to rule on a dispute, providing a fixed amount of reward and punishment. The lack of sophistication is addressed in this paper with a novel fuzzy RSU controller considering the severity of incident, driver past behaviour, and RSU confidence to determine the reward or punishment for the conflicted drivers. Although any driver can lie in any situation, it is expected that trustworthy drivers are more likely to remain so, and vice versa. This behaviour is captured in a Markov chain model for sender and reporter drivers where their lying characteristics depend on trust score and trust state. Each trust state defines the driver's likelihood of lying using different probability distribution. An extensive simulation is performed to evaluate the performance of the fuzzy assessment and examine the Markov chain driver behaviour model with changing the initial trust score of all or some drivers in Veins simulator. The fuzzy and the fixed RSU assessment schemes are compared, and the result shows that the fuzzy scheme can encourage drivers to improve their behaviour.<|reference_end|>
@article{shahariar2024a,
    title={A fuzzy reward and punishment scheme for vehicular ad hoc networks},
    author={Rezvi Shahariar, Chris Phillips},
    journal={arXiv preprint arXiv:2405.04892},
    year={2024},
    doi={10.14569/IJACSA.2023.0140601},
    archivePrefix={arXiv},
    eprint={2405.04892},
    primaryClass={cs.CR}
}