# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 2 Related Work  
Recently, user recruitment algorithms have attracted the attention of many scholars, who have proposed extensive promising studies in MCS systems. Most of them are devoted to finding the optimal solutions to recruit the most appropriate participant to complete the tasks. [PERSON] et al. [40] focused on data diversity and proposed a unified visual crowdsensing framework called UtiPay based on the microscopic and macroscopic visual task types to improve the quality of group sensing data. In [41], to complete the task with the minimum cost, [PERSON] et al. defined the \"t-sweep k-coverage\" crowdsensing tasks and designed a user recruitment algorithm based on the theory of greedy strategy. After simulation experiments on real data sets, the superiority and effectiveness of this method were proved. [PERSON] et al. [42] fully exploited the content information together with the context information to model workers' preferences on tasks accurately. Then they proposed a novel personalized task-oriented worker recruitment mechanism for mobile crowdsensing based on a careful characterization of workers' preferences. One of the most important challenges in MCS is the coverage quality for some applications. Especially in urban scenarios, coverage has become one of the criteria to measure the completion quality of tasks. In [43], [PERSON] et al. proposed a novel user recruitment that only selected a subset of mobile participants to achieve the maximum spatial coverage with a constrained budget. The results revealed that the algorithm achieved a near-optimal solution compared with the brute-force search results. However, this method would spend too much running time and cost a lot for the platform. [PERSON] et al. [44] jointly considered the parameters, such as user localization, mobility traces, and reputation, to design a two-steps novel Stable Data-based Recruitment System (SDRS) for localization tasks. Meanwhile, they took into account the range-free sensors and the mobility of workers to improve the coverage quality of tasks. The results indicated that the proposed method could attain a speedy localization with better quality compared with other benchmarks. [PERSON] et al. [45] tried to tackle the issues of finding task assignments that could make the optimal tradeoff between coverage-aware preferences of service requesters and profit-basedpreferences of workers under the budget constraints in MCS. The simulations showed that the proposed algorithms mostly provided substantially better task assignments in terms of user happiness and coverage quality. With the continuous development and maturity of edge computing technology, to further improve the task completion rate and reduce the load of the cloud platform, many researchers have introduced edge computing into MCS. [PERSON] et al. [46] proposed two privacy-preserving reputation management schemes for edge computing enhanced MCS to preserve privacy and deal with malicious participants simultaneously. The experiments demonstrated that both schemes achieved high-cost efficiency. In [47], [PERSON] et al. studied dynamic user recruitment under the scenarios for different sensing tasks. For the long-duration task and short-duration tasks, they presented two different user recruitment algorithms with edge-aided MobileCrowdsensing. The experiments showed the proposed user recruitment could achieve better validity and reliability. To solve the problem that user recruitment failed to effectively address the task requirements or the relevant maximization and diversification, [PERSON] et al. [48] designed a task-oriented user selection incentive mechanism (TRIM) in an effort toward a task-centered design framework in edge-aided MCS. The simulation results indicated that TRIM could achieve feasible and efficient user selection. However, the previous works have presented outstanding simulation results, and there are several shortcomings related to user recruitment in mobile crowdsensing (MCS) systems.