# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 3 System Model and Problem Formulation  
### System Model  
The MCS system generally is comprised of mobile users \(U\) and the cloud platform \(P\). However, in a dynamically changing environment, especially an urban environment, except for a large number of mobile people, there are still quite a few pre-deployed static nodes with powerful sensing, collecting, computing, and communicating capabilities, such as smart lampposts, signal base stations, smart camera and so on, which also can collect the sensing information. Hence, we not only utilize the mobile users to gather relevant information and complete the tasks; furthermore, we take advantage of these edge nodes as a supplement to build a new CrowdSensing system. As shown in Figure 1, our system consists of a three-layer hierarchical network.  
1. **Cloud layer**. The Cloud layer is responsible for the publication of the tasks, and it provides computing and analyzing resources for collecting data. As a centralized controller, the cloud platform gets the tasks from some applications or requesters, then publishes these tasks to the users. Meanwhile, the cloud platform will enable interactions with the other two layers and manage the user's personal information, including historical information, locations, and reputations. In our system, the cloud platform will establish a user reputation module to select the proper users based on their historical performance to complete the tasks efficiently and qualitatively. Additionally, the cloud layer is also a data processing center that will analyze and summarize these sensed data, then package it to the applications and requesters.
2. **Edge layer**. As depicted in Figure 1, the edge layer is located between the cloud platform and the mobile sensing end devices. Due to powerful computing and communicating capabilities, the pre-deployed static nodes would be the perfect edge nodes in the CrowdSensing system, which can support and enhance the performance of the mobile end devices. In the urban environment, edge nodes can be replaced by smart static nodes such as smart lampposts and base stations. Heterogeneous mobile devices would be connected to the cloud platform through the edge layer closer to the users in 5G/6G or WiFi, which can release the computing pressure on the cloud, reduce the delay and increase the storage space of information.
3. **Sensing layer**. As the most important and basic layer of the CrowdSensing system, mobile users who take the intelligent terminals with many embedded sensors are the key component in the sensing layer and play a fundamental role in sensing and collecting various data at a certain time in specific locations. We assume that all users volunteer to participate in tasks and that sensed data are accurate. The random mobility of mobile users greatly enhances the flexibility of ubiquitous sensing, and the static nodes can ensure the coverage of the sensing network. The collaboration and interaction between mobile and static sensors could promote the performance and quality of sensing and data processing.  
Figure 1: Edge-aided MCS system.