Multi-skill collaboration-based task assignment in spatial crowdsourcing

Abstract

In recent years, with the rapid development of mobile terminal devices and the Internet, spatial crowdsourcing has received widespread attention. The spatial crowdsourcing problem is characterized by the location information contained in the attributes of workers and tasks, the crowdsourcing platform can assign reasonable spatial tasks to workers based on their current location, and the execution of tasks will be accompanied by dynamic changes in their physical location of the workers, so task assignment is an important research content of spatial crowdsourcing problem, and the quality of task assignment methods can affect the development of its crowdsourcing platform. For the spatial crowdsourcing problem that requires a group of workers with relevant professional skills to work together to complete special application scenarios (e.g., performance-type tasks), a cost-based greedy approach is proposed to minimize platform costs by matching a suitable team of workers for spatial tasks under the constraints of workers and tasks. Extensive experiments have been conducted on synthetic datasets to demonstrate the effectiveness and efficiency of the proposed approach.