A Server-Assigned Spatial Crowdsourcing Framework

摘要: With the popularity of mobile devices, spatial crowdsourcing is rising as a new framework that enables human workers to solve tasks in physical world. crowdsourcing, goal crowdsource set spatiotemporal (i.e., related time and location) workers, which requires physically travel those locations order perform tasks. In this article, we focus on one class send their server thereafter assigns every worker proximity worker’s location with aim maximizing overall number assigned We formally define maximum task assignment (MTA) problem identify its challenges. propose alternative solutions address these challenges by exploiting properties space, including distribution cost workers. MTA based assumptions all are same type equally qualified performing Meanwhile, different types may require various skill sets or expertise. Subsequently, extend taking expertise into consideration. refer score (MSA) show practicality generality. Extensive experiments synthetic two real-world datasets applicability our proposed framework.