# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 5 Simulation and Numerical Results
When we set tasks = 100, users = 10,000, and edge nodes = 50, it can be seen from Figure 5 that the task completion rate and task coverage rate increase substantially with the total budget growing. Since PRSQ and B-WSN do not consider the budget, as the value of the total budget changes, the performances of the task completion rate and task coverage rate of PRSQ and B-WSN remain constant. While our RBUR, RUCR, and BRD-ENUR take the budget as an important consideration, the three curves remain in an upward state with  
Figure 4: Performances under various numbers of edge nodes. (**a**) Task Completion Rate; (**b**) Task Coverage Rate. Figure 3: Performances under various numbers of users. (**a**) Task Completion Rate; (**b**) Task Coverage Rate. the increasing value of the budget, and RBUR is very close to BRD-ENUR. When the budget is equal to 100, we can find that RUCR and BRD-ENUR only achieve 45% and 39% task completion rates in Figure 5a, meanwhile 30% and 26% task coverage rates in Figure 5b. With the increasing budget, the performances have a rapid growth when budget = 200. The reason is that the limited budget will reduce user enthusiasm, and the task rewards may not cover the user costs. When the total budget expands, more users will be more willing to take part in the tasks, so the platform will have a greater possibility to select the suitable user for each task. Compared with the BRD-ENUR, under the same condition, our RUCR effectively combines the capabilities of mobile users and edge nodes so that this collaborative sensing method can provide strong guarantees for task completion and task coverage. Therefore, our RUCR is more excellent than the BRD-ENUR approach, no matter the value of the budget. In Figure 6, we verify the relationship between the performance of the task completion rate and task coverage rate with the varying number of tasks. Through the comparison with the other three algorithms, our RCUR displays a better performance, and all of the algorithms show a downward trend with the number of tasks increasing. When the number of tasks is equal to 50, it is obvious that our RCUR significantly outperforms the other three benchmark algorithms. However, when the number of users and edge nodes remains unchanged, more tasks mean that there are not sufficient users or edge nodes to be candidates, which cannot guarantee the task completion rate. Furthermore, the platform may not find the most suitable participant to complete the task and ensure coverage. From Figure 6a, it can be seen that when the tasks increase to 250, our RCUR decreases from 87% to 59% in terms of the task completion rate but is still far superior to other algorithms. The results in Figure 6b are just as we expected that our RCUR takes a huge advantage in the performance of task coverage rate. In Figure 7, we compare the task completion rate and task coverage rate of our RCUR approach with the other three algorithms under the different values of the initial user reputation. As we know, We except for the B-WSN algorithm, the other algorithms regard the user reputation as a fundamental consideration and recruit the participant based on the user reputation. However, our RBUR and RCUR not only evaluate the user's past reputation scores according to historical performance. Furthermore, it also takes account of the instantaneous reputation score and combines them to attain the user reputation. Figure 7a indicates that the performance of these algorithms has a slow growth with the initial reputation increasing. However, our RBUR and RCUR occupy the leading position  
Figure 5: Performances under various values of budget. (**a**) Task Completion Rate; (**b**) Task Coverage Rate. in terms of task completion rate. When the value of a user's initial reputation increases, it means that there will be more users who can meet the requirement of the tasks and can be potential participants in finishing the task.