# Evaluation of a Reputation Management Technique for Autonomous Vehicles
## 7 Results
This may be mitigated using other techniques to detect and respond to malicious users. Now, the impact of the number of witnesses is assessed. This considers the impact of a key variable that would change from environment to environment. Figure 8 shows that having greater numbers of witnesses results in a negligible change in operating error but an increase in reporting error. While it was initially hypothesized that increasing the number of witnesses would diminish both operating and reporting error, this was not the case; in fact, the reporting error had a noticeable increase. The implication from these results is that having more witness introduces a greater risk of including a malicious vehicle as a witness. Even a single malicious vehicle can influence the operating and reporting error from a transaction, since malicious vehicles intentionally score transactions inaccurately and reputation is calculated using weighted averages. We observe that having a malicious vehicle misreport a transaction has a greater effect on the reporting error than on the operating. This can be attributed to the fact that, for operating error, malicious vehicles may only influence the reputation of the broadcasting vehicle who sent the transaction;  
Figure 7: Average operating and reporting error from having a different proportion of normal vehicles. however, for reporting error, malicious vehicles have an influence of equal magnitude on every witness, since reporting reputation is based on a witness's deviation from the average score of the transaction. For this reason, a single malicious vehicle also has a larger impact on the reporting error than the inclusion of another normal vehicle. Next, the impact of considering witness reputation was explored. Figure 9 shows the results of the simulation being run with the reputation of witnesses factoring into the overall score of each transaction. Under this approach, witnesses with lower reputations had a reduced impact on the transactions they scored, while witnesses with higher reputations had a greater impact. When implementing this weighting method, there was a noticeable decrease in both operating and reporting error in the simulation. This highlights the value of reducing the effect that lower reputation vehicles have on the network by proactively devaluing their input. Furthermore, once the reputation of malicious vehicles decreases, they no longer possess the same effect on the reputations of other vehicles. Figure 8: Average operating and reporting error from having a denser population (i.e., with a greater proportion of vehicles acting as witnesses). Figure 9: Average operating and reporting error from weighting transactions, based on the reputations of their witnesses. A possible addition to the model would be to nullify the effect that vehicles who fall below a fixed reputation threshold have on the scoring of subsequent transactions. In doing so, malicious (or even faulty) autonomous vehicles would have a minimal effect on the reputation of other nodes, once their own reputation is low enough. However, this implementation would need a remediation mechanism for vehicles that cease to behave maliciously. For example, a successful impersonation attack has the potential to furnish the reputability of a normal vehicle. To ensure normal vehicles, such as these, are not permanently penalized, which would hinder the consensus-based integrity of the model, vehicles could be allowed re-entry after consistent demonstration of normal behavior. Focus now turns to the impact of considering the number of witnesses in ratings. Figure 10 shows the results of the simulation being run with and without the byNumWitnesses parameter. This parameter, when true, weights the transactions, in averaging, based on their number of witnesses. In the context of the simulation, this causes transactions with a larger number of witnesses to have a greater effect on the operating and reporting reputations of the participating vehicles.