# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 1 Introduction
Experimental results indicated that the method could effectively ensure the quantity and the quality of users' participation. However, the complex computation would lead to time delays and information propagation. To guarantee the coverage quality of the tasks, from the perspective of the tasks, authors in [39] focused on the task attributes, and divided the whole sensing region by a weighted Voronoi diagram, then proposed a novel willingness-aware user recruitment strategy (WAUR) for MCS. Simulations revealed that the approach could significantly improve the performance compared with other algorithms. While these studies above integrated and interweaved many different variables, however, they ignored the most important part of MCS--the uncertainty of mobile users. The users' random mobility and uneven distribution could not enable the cloud platform to have enough users to perform the tasks, but also would bring about a shortage of users for the tasks when facing emergencies and unpredictable tasks, which leads to an unnecessary accuracy loss. More seriously, some users may upload fake data for compensation which would critically decrease the quality of tasks. Furthermore, although numerous mobile devices can replace traditional sensors, the instability and uncertainty of these mobile users may affect the completion and quality of tasks. Selecting reliable users and completing tasks more effectively has become a huge challenge in MCS. Hence, to avoid the fraud of users and ensure the task coverage quality and improve the task completion rate, by jointly considering the user reputation and edge computing, we present a reputation-based collaborative user recruitment algorithm (RCUR) for edge-aided Mobile Crowdsensing with the goal of improving the completion rate and maximizing the spatial coverage of tasks under temporal and budget constraints. The main contributions of our work are summarized as follows:  
1. Existing MCS systems in urban scenarios suffer from high delay and limited storage space, which negatively affect task completion rate and spatial coverage. To address these issues, we introduce edge computing into MCS and develop an edge-aided MCS system in urban scenarios. Our system adds an edge layer that efficiently handles the computing pressure and storage needs while leveraging the powerful sensing abilities of edge nodes. Moreover, the user recruitment problem is formulated with spatial and budget constraints to improve completion rates and maximize spatial coverage. 2. Prior methods for user selection in MCS systems fail to consider user reliability and credibility adequately. To address this gap, we establish a user reputation module to evaluate the user's past reputation score based on previous performance. Then we jointly consider the distance and coverage ability of the user to calculate the instantaneous reputation score, which is combined with the user's past reputation score to generate an overall reputation equation. Our cloud platform selects the proper users according to their reputation scores to complete the tasks efficiently and qualitatively. 3. In order to achieve a better task completion rate and maximum spatial coverage, we propose a reputation-based collaborative user recruitment algorithm (RCUR) in an edge-aided Mobile CrowdSensing system under certain budgets. Our innovative approach leverages the user reputation method to identify suitable participants who can guarantee the coverage quality of the tasks. Additionally, we use collaborative sensing to select the edge nodes to finish the tasks and ensure coverage when lacking potential users. Moreover, we design a greedy method to recruit the optimal participant for each task. Our simulations and experimental results verify that our proposed RCUR algorithm is outstanding compared with other methods in terms of task completion rate and spatial coverage. The rest of the paper is organized as follows. In Section 2, we review and summarize related work. Section 3 presents our system model and problem formulation.