# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
## 5 Performance Evaluation
### Performance of the Reputation-Based Incentive Mechanism  
We first assume that all pools are capable of reaching the optimal mining state, including honesty, fluctuations, entry thresholds, rewards, and reputation. Subsequently, focusing on the mining rounds required to reach the best state reflects the benefits offered by the proposed scheme.  
1. In Figure 3, the increase in the number of mining rounds will lead to an increase in the proportion of honest miners. This mainly results from the high reputation increasing the probability of mining and obtaining incentive, which will attract more candidate miners to become involved in the current mining pool. Thus, there will be more honest miners in the pool under the presence of the reputation threshold. Again, the horizontal axis represents the number of mining rounds required to achieve total honesty. Moreover, while the mining rounds remain unchanged, a high reputation threshold also means lower honesty. This is because the low-reputation miners can be eliminated as the reputation threshold increases. In addition, more rounds are required for the mining pool to reach the optimal state. This also confirms that the formulated algorithms can produce a positive effect on maintaining honest behavior and improving the level of reputation.  
(ii) Figure 4 shows the relationship between the number of fluctuating miners in the pool and mining rounds. As the mining rounds increase, the fluctuating miners decrease: first, it decreases faster reflecting the elimination of miners who met the fluctuation threshold; then, most miners take honest actions and the probability of being eliminated decreases. Thus, the lower reputation entry threshold will encourage more miners join the pool and decrease the number of fluctuating miners. Similar to Figure 3, the higher threshold leads to less mining rounds to arrive at the smallest fluctuation.  
(iii) Figure 5 illustrates the total revenue of the pool as the number of mining rounds increases. As the number of honest miners \(|\mathcal{H}^{(i)}|\) in the pool increases, the total benefit also increases. Furthermore, the faster increase is a result of the lower reputation entry threshold. In addition, if we were to fix the budget, the threshold size would directly determine the time required to reach the optimal reward, that is, smaller thresholds are able to rapidly produce a peak.  
Figure 3: Mining rounds vs. Honesty.  
(iv) In Figure 6, it can be found that the entry threshold reputation produces a greater impact on the number of mining rounds, while the fluctuation reputation has little effect. Note that this happens while the number of mining rounds required and the proportion of honest pool miners reach the optimal level. We can also observe the changes in curve fluctuation caused by different fluctuation values.  
Figure 4: Mining rounds vs. Fluctuation.  
Figure 5: Mining rounds vs. Reward.  
(v) Figure 7 illustrates the relationship between the number of vehicles participating in the calculation and the overall pool reputation. As the amounts of players increases, the sum of their reputations will gradually increase. Moreover, the slope gradually decreases before it starts to slowly decrease. This is mainly due to the fact that: when an increasing number of vehicles have just joined the pool, the reputation level will increase; however, once the number of vehicles exceeds the peak, dishonest miners are eliminated as a result of the joining threshold implemented, and then a slow downward trend begins.