# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
## 5 Performance Evaluation
### Algorithm Performance  
Consider three pools named A, B and C, in which Algorithms 1 and 2 are deployed to the pool B and C. Among them, pool A adopts the general mining pool random algorithm (the original algorithm used in NiceHash platform [42])). Note that any miners who successfully reached the entry condition of pool A can select to join A and make the mining decisions, while miners who join pool B and C need to satisfy the conditions of Algorithms 1 and 2.  
(i) As shown in Figure 11, once the iteration progresses, the effect of Algorithm 1 on the mining pool converges and fluctuates continuously. This is mainly because in each round of mining, miners who have completed mining choose to enter/exit the current pool/blockchain, based on their incentive and reputation. Obviously, the peaks and troughs of the proposed Algorithm 1 generates better effects. We can find that the averages of A and B are approximately equal to 14 and 17, respectively. The main reason for this is that under the action of Algorithm 1, miners demonstrating low-reputation and dishonest behaviors cannot meet the access conditions of B. Similarly, in Figures 12 and 13, it can be easily concluded that the probability of successful mining and the miner's income (with equal reputation and computation capability) generated by B have improved, i.e., Algorithm 1 is better than that of the general random sampling algorithm. Further, compared with the Figure 14, the mean value of C is about 20, and \(C>B>A\), which means that the blockchain-based crowdsourcing system based on the spatial reputation has produced better positive effects.  
Figure 11: Iterations vs. The utility of pool.  
Figure 10: Vehicles vs. Data authenticity.  
Figure 14: Iterations vs. The utility of pool.  
Figure 12: Iterations vs. The probability of pool.  
Figure 13: Iterations vs. The utility of miners.  
(ii) In Figure 14, as the iterations increase, pool C can obtain significantly higher returns than A. This is mainly because Algorithm 2 mainly excludes low-reputation miners in each round, so the total reputation of the pool C is higher than A, which also leads to higher returns for the mining pool. In addition, pool C has a higher overall probability of successful mining in Figure 15. Based on the blockchain consensus mechanism, this also means that the computational capability of pool C is stronger, and it also shows the positive correlation between reputation, computational capability and profits. We then choose two miners with equal reputation and computational capability in A and C, respectively, to illustrate the profits of pool miners in Figure 16. It also shows that high-reputation miners obtain higher profits in pool C, mainly because pool A contains some low-reputation/dishonest miners, which damages the overall mining capacity and reputation of pool A. In addition, after analyzing the profits of single miner, it is found that the overall trend in C is one of decline, mainly because when we set a threshold for the iterations to screen low-reputation, the reputation of miners in C were successively evaluated. The reduced reputation value, however, is still higher than A, which also validates the assumed model.  
Figure 16: Iterations vs. The utility of miners  
Figure 15: Iterations vs. The probability of pool.  
(iii) In addition, comparing B with C, C is better than B in terms of utility, probability of successfully and miner's profits. That is because Algorithm 2 has stricter conditions and thresholds when screening low-reputation miners, and thus produces better results. Further, since the security and efficiency of the blockchain structure are mainly determined by the computational capability, this also provides supporting evidence that the proposed algorithms can enhance system security and improve efficiency.