# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 1 Introduction  
With the rapid development of sensing, computing, and communicating technologies, intelligent devices equipped with multifarious sensors, for example, mobile phones, iPads, and wearable devices have already played a crucial role in human life, which makes the people who take these intelligent devices to become powerful sensing nodes [1, 2, 3]. The inherent mobility nature of people has empowered and inspired the people to take part in ubiquitous sensing, and the rich sensing capabilities of sensor-enhanced devices make pervasive computing possible, which stimulates the emergence and promotes the development of an appealing paradigm named Mobile CrowdSensing (MCS) [4]. MCS enables and inspires a vast number of people to sense and contribute data; therefore, it has become a convenient method for many Internet of Things (IoT) applications, such as smart cities [5, 6, 7], environmental monitoring [8, 9, 10, 11, 12], smart transportation [13, 14] and intelligent medicine [15, 16], which improves work efficiency and quality of our life. Compared with traditional sensing technology, the core idea of MCS is to utilize people's mobility and recruit people with intelligent devices to meet the requirements and complete sensing tasks. There is no need to deploy massive static nodes in the sensing areas, which can largely reduce installation costs [17]. Moreover, the widespread popularity of intelligent devices can provide strong guarantees for spatial coverage and task completion quality, even when facing emergencies and unpredictable tasks. Meanwhile,the outstanding computing capabilities of mobile devices can conduct the computation for the cloud platform, which has been widely used in many real-time scenarios, such as autonomous driving [18; 19; 20]. However, the inadequate resources of mobile devices are unable to execute the complex computation of large amounts of real-time information, which would lead to the latency of data and information propagation [21]. To address the issues, some researchers introduced Edge Computing into the MCS system, whose basic idea is to add edge nodes, such as smart lampposts [22; 23] and base stations [24], with sufficient energy, excellent storage, and computing capabilities at the edge of the network. So sensing data are computed and processed by the closest edge nodes instead of the cloud platform, which is promising to effectively reduce the time delays and computational complexity in cloud platforms. However, how to take full advantage of the edge nodes more reasonably has aroused great interest from researchers [25; 26]. Although MCS is an appealing sensing paradigm and shows great potential, it has raised plenty of challenging issues, for example, user recruitment algorithms [27; 28], incentive mechanisms [29; 30], task allocations [31; 32; 33], and data fusion [34; 35]. As one of the most vital challenges in MCS, the user recruitment algorithm refers to the problem of how to recruit appropriate participants from extensive potential users to meet the various requirements and complete the tasks under some constraints [36]. More and more researchers have devoted plenty of effort to finding optimal methods to solve this problem by comprehensively considering various factors. In [37], [PERSON] et al. jointly considered the effects of users' willingness, reputation, and activity, then proposed a user characteristics aware participant selection (UCPS) mechanism to improve the task completion rate and ensure the quality of sensing data in different regions. The results showed that the mechanism could improve the task completion rate and data quality. However, they ignored the situation of potential users in the task area. Taking into account the requirements of both quantity and quality of users' participation, [PERSON] et al. introduced the reputation evaluation model and proposed a crowdsensing task selection algorithm to encourage participants to make the greatest contributions [38].