# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 5 Simulation and Numerical Results  
In this section, extensive simulations are performed to verify our proposed reputation-based user recruitment algorithm (RBUR) and reputation-based collaborative user recruitment algorithm (RCUR), then compare the performance with the other three benchmark methods. We apply the RCUR algorithm to the custom simulator, which is called CrowdSen-Sim [50], to help us conduct the experiments in realistic urban scenarios. In our simulation, we select a rectangular area of 10 km \(\times\) 10 km in the real city of Ottawa as the experimental space similar to Figure 2, and there are three important components which are users, tasks, and edge nodes. The mobile users who take the intelligent devices are randomly distributed and wander along the streets in the city at a speed of \(1m/s\) in this area. The number of mobile users ranges from 5000 to 25,000, and the relevant details \(F_{u}=\left(l_{u_{i}},R_{u_{i}},\sigma_{u_{i}},C_{u_{i}}^{\theta_{j}}\right)\) of the mobile users \(U\) will be set up randomly before the experiments start. In the beginning, the users' reputation scores are the same. As a user completes tasks, the value of their reputation score will change based on the quality of the completed tasks. Meanwhile, there are some edge nodes pre-deployed along the street in this area, and we set the key parameters according to [22]. Then the cloud platform will publish a certain number of tasks at different locations with different budgets in this area, which varies \([50,250]\) and divide each task into 10 timeslots, in which the cloud platform will select the best suitable user or edge node based on the fitness function to complete the tasks. We list the detailed experimental settings in Table 2.  
In our study, we choose three benchmark algorithms, including a participant recruitment method aiming at service quality (PRSQ) [51], a budget re-distribution algorithm for edge nodes user recruitment (BRD-ENUR) [47], and a basic wireless sensor network (WSN) sensing method (B-WSN). Then we compare them with our proposed RBUR and RCUR algorithms to verify their effectiveness and efficiency. The PRSQ algorithm constructed a quality of service model based on the accumulated reputation and willingness of participants, then selected the most suitable participants to ensure the QoS. The BRD-ENUR introduced edge computing into MCS. Then it estimated the quality of sensing data accord \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of users & [5000, 25,000] \\ Number of tasks & [50, 250] \\ Coverage radius of task & [20, 50] \\ Number of edge nodes & [30, 70] \\ Budget & [100, 200] \\ Initial user reputation & [0.2, 1.0] \\ Cost & [0, 2.0] \\ Sensing radius of user & [5, 25] \\ Sensing radius of edge nodes & [20, 30] \\ Evaluation period & 8:00 AM–20:00 PM \\ Task duration & 30 min \\ Timeslot duration & 3 min \\ Balancing coefficient & \(\alpha,\beta,\omega,\lambda\) & 0.6, 0.6, 0.5, 0.7 \\ \(\eta,\mu,\varphi\) & 0.3, 0.4, 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental settings. Figure 2: Sensing area. ing to the participant's reputation and proposed the algorithm to recruit users statically. B-WSN was the fundamental sensing algorithm and chose the static nodes to complete the tasks. The differences between these five algorithms are listed in Table 3. Finally, we perform the experiments over 100 runs and obtain the average values to compare these five algorithms in some metrics, such as the task completion rate, task coverage rate, etc. As we know, the tasks will be divided into 10 timeslots. We assume that when more than half of the timeslots of one task are finished by the participants, we can define the task as completed. So we can calculate the overall task completion rate based on the completion of the task's timeslots.