# Evaluation of a Reputation Management Technique for Autonomous Vehicles
## 9 Future Work  
There are many directions that work in this area could take in the future. For example, the model's complexity could be enhanced to simulate more realistic interactions. The model assumed vehicles to perform consistently throughout the duration of the simulation, with normal and malicious vehicles invariably sending mostly credible and incredible transactions, respectively. Real-world occurrences, such as vehicle electronic hijacking or changes in ownership, may violate that assumption, as a vehicle's behavior may change spontaneously or over time. While these occurrences were considered by weighting transactions by their recency, such occurrences were not implemented in the simulation; therefore, it was not possible to evaluate whether the system addressed these concerns adequately. The model performed well in environments with a low concentration of malicious vehicles; however, the possibility of abnormalities arising necessitates that a cybersecurity system for AVs vehicles remain robust under other conditions. For example, botnet attacks, in which infected nodes exponentially reproduce malware to infect other nodes [53], or even manufacturing blunders that cause a large number of AVs to be defective, may cause networks to have a large proportion of malicious vehicles. Future work can also add additional simulation system complexity to model abnormal behavior, such as vehicle-herding, in which a small group of malicious vehicles travel together to reinforce their own reputations and depreciate others'. The parameter to weigh transactions based on the number of witnesses had a negligible impact in the simulation but may have been more relevant if applied to conditions like vehicle-herding, in which a few malicious vehicles can have a disproportionately harmful effect. Additional research is planned to assess the efficacy of using a blockchain backend to circulate reputation information in a decentralized framework among RSUs and base stations. The efficacy of the blockchain implementation could also be compared to that of other data storage systems, transmission methods, and decentralized database systems. The system could also be integrated with the ECLIPSE urban mobility simulation system to evaluate more complex scenarios. Additional research, regarding the implementation of the trust management technology, as part of real world and prototype roadside unit and regional systems, would also be beneficial. It will be needed to further assess the technology, over time, as the hardware and software used for these systems mature. The development of an effective reputation management system plays a crucial role in preserving the integrity of data in a network of autonomous vehicles. Reputation and, more broadly, trust have key implications on the functionality of autonomous vehicles in the real world. Trust is key to enhancing vehicles' decision-making capabilities to distinguish the information that should be reasonably utilized from communications that can be safely discarded. This may be crucial in preventing vehicular collisions and other traffic accidents at the hands of malicious actors. Autonomous vehicle pathfinding is another process that may be corroborated or altered, depending on the credibility of the roadside information the vehicle receives, such as traffic and weather reports. Furthermore, insurance companies may find the reputation of their insureds pertinent to influencing vehicle owners' premium rates, since having a history of credible transactions may imply a lower likelihood of being involved in traffic accidents. However, a vehicle's reputation is not necessarily indicative of its intentions. Manufacturing errors, faulty firmware updates, and other innocuous mishaps may all affect the reputation of a vehicle. Therefore, preserving a reliable ledger of reputation information may also aid in identifying such oversights and making subsequent improvements, without vehicle owners being penalized. Conceptualization, D.K., S.U., A.B. and J.S. ; methodology, D.K., S.U., A.B. and J.S.