# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 6 Conclusions and Future Work  
The emergence of the Internet of Things and the widespread popularity of intelligent mobile devices enable Mobile CrowdSensing (MCS) to be a convenient platform for many applications. Meanwhile, the introduction of edge computing has also accelerated the development of MCS. However, how to make MCS more efficient and utilize edge computing technology has raised huge challenges. Hence, designing a reasonable user recruitment algorithm to find suitable users and take full advantage of edge nodes in order to improve the task completion rate and coverage rate has become increasingly important and urgent. In this study, we propose a reputation-based collaborative user recruitment algorithm (RCUR) under a certain budget in an edge-aided Mobile CrowdSensing system. We first introduce edge computing into MCS and build an edge-aided MCS system in urban scenarios. Moreover, we analyze the influence of user reputation on user recruitment. Then we establish a user reputation module to evaluate the user's past reputation score based on the previous performance, which indicates the users' reliability and credibility. Then we jointly consider the distance and coverage ability of the user to calculate the instantaneous reputation score. Thus, we deduce the user reputation equation by combining the user's past reputation score with the instantaneous reputation score. Finally, we utilize the sensing ability of edge nodes and design a collaborative sensing method. We use the greedy method to help choose the appropriate users for the tasks. Simulation results compared with the other three algorithms prove that our RCUR approach can significantly achieve better performance in task completion rate and task coverage rate.  
A dynamic user recruitment algorithm is one of our future works. Generally speaking, user recruitment always recruits one participant to accomplish a certain task. However, when the tasks require more users to sense these data, how to design an optimal algorithm to select a set of users has been an important problem in MCS. Meanwhile, how to dynamically determine the number of required users for the tasks according to the user's characteristics, such as cover ability, reputation, and cost, is also a tough problem. In the future, we will concentrate on this dynamic user recruitment in MCS.  
Figure 8: Performances under various values of user sensing radius. (**a**) Task Completion Rate; (**b**) Task Coverage Rate.  
**Author Contributions:** Conceptualization, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and W.C.; methodology, [PERSON] ([PERSON]) and W.C.; software, [PERSON] ([PERSON]), W.W. and J.Y.; validation, [PERSON] ([PERSON]) and W.W.; formal analysis, [PERSON] ([PERSON]) and W.C.; investigation, [PERSON] ([PERSON]), W.W. and J.Y.; resources, [PERSON] ([PERSON]) and W.W.; data curation, [PERSON] ([PERSON]) and J.Y.; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and W.C.; supervision, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and W.C.; project administration, [PERSON] ([PERSON]) and W.C.; funding acquisition, [PERSON] ([PERSON]) and W.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Fundamental Research Funds for the Central Universities (3102017 zy026).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.