# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 5 Simulation and Numerical Results
Moreover, according to Equation (1), the coverage rate of one task can be defined as the value of the sensed coverage area by user or edge node divided by the expected sensing coverage area of the task in certain time limitations. In Figure 3, we evaluate the performance of these five algorithms under the various number of users when we set the tasks = 100, edge nodes = 50, and budget = 150. From Figure 2(a), we compare the task completion rate and find that our proposed RCUR is more outstanding than the other three benchmark algorithms. The RBUR is closer but better than the PRSQ and BRD-ENUR. In contrast, B-WSN maintains a relatively low task completion rate. Figure 2(b) demonstrates that our RCUR also can achieve a higher task coverage rate than other algorithms. By taking into account multiple factors, the RBUR has a better result than the PRSQ and BRD-ENUR. Since B-WSN depends on the sensing and cover ability of the edge nodes, which are less numerous than mobile users, it only selects edge nodes to sense these data and complete tasks. As the number of mobile users increases, MCS algorithms will have a larger user pool to find the most suitable user to complete each task. The more mobile users that participate, the higher possibility the tasks can be completed. Even more, a more covered area can be attained. As a result, when the number of users ranges from 5000 to 25,000, the performance of three algorithms in MCS shows an upward trend both in the task completion rate and the task coverage rate. However, our proposed RCUR approach not only recruits the appropriate mobile user to finish tasks but also utilizes the edge nodes as a supplement when there are no available users in some sensing areas. Hence, the performance of RCUR is better than others. In Figure 4, when we set tasks = 100, users = 10,000, and budget = 150, we tried to discuss how the task completion rate and task coverage rate change when the number of edge nodes increases. Figure 4 illustrates that there is a huge difference between our proposed RBUR, RCUR, and the other three algorithms. Although BRD-ENUR introduces edge nodes into the MCS system, it only takes these nodes as edge platforms which can reduce storage and computing load in the cloud platform. RCUR and B-WSN make full use of the enhanced sensing ability of these edge nodes to sense the data and cover the sensing areas. Therefore, when there are more and more edge nodes in the sensing areas, each task will have more options to find the optional solution, which can give rise to a great improvement in the task completion rate and task coverage rate. From Figure 4, \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Sensing** \\ **Participants** \\ \end{tabular} } & \multirow{2}{*}{**Key Factor**} & \multirow{2}{*}{**Advantage**} \\ \hline \multirow{2}{*}{RBUR} & Mobile users & & \\  & & & \\ \hline \multirow{2}{*}{RCUR} & Mobile users, & & \\  & edge nodes & & \\ \hline \multirow{2}{*}{PRSQ} & Mobile users & & \\  & & & \\ \hline \multirow{2}{*}{BRD-ENUR} & \multirow{2}{*}{Mobile users} & & \\  & & & \\ \hline \multirow{2}{*}{B-WSN} & \multirow{2}{*}{edge nodes} & & \\  & & & \\ \end{tabular}
\end{table} 
Table 3: Differences between these four algorithms. we can see that when the edge nodes are equal to 30, RUCR is slightly better than PRSQ and BRD-ENUR both in the task completion rate and task coverage rate. The RBUR remains unchanged, while the B-WSN stays at a low value. With the number of edge nodes growing, Figure 4 indicates that RCUR is improved from 72% to 85%, and B-WSN has a great improvement from 38% to 66% in task completion rate. The reason is that while our RCUR takes the edge nodes as a collaborative sensing method, it mainly recruits mobile users to finish the tasks. Consequently, it does not attain much improvement over the B-WSN. In Figure 4, the task coverage rates of RCUR and B-WSN also present an increasing trend, and our RCUR always outperforms other algorithms.