Multi-skill aware task assignment in real-time spatial crowdsourcing

With the development of mobile Internet and the prevalence of sharing economy, spatial crowdsourcing (SC) is becoming more and more popular and attracts attention from both academia and industry. A fundamental issue in SC is assigning tasks to suitable workers to obtain different global objectives. Existing works often assume that the tasks in SC are micro and can be completed by any single worker. However, there also exist macro tasks which need a group of workers with different kinds of skills to complete collaboratively. Although there have been a few works on macro task assignment, they neglect the dynamics of SC and assume that the information of the tasks and workers can be known in advance. This is not practical as in reality tasks and workers appear dynamically and task assignment should be performed in real time according to partial information. In this paper, we study the multi-skill aware task assignment problem in real-time SC, whose offline version is proven to be NP-hard. To solve the problem effectively, we first propose the Online-Exact algorithm, which always computes the optimal assignment for the newly appearing tasks or workers. Because of Online-Exact's high time complexity which may limit its feasibility in real time, we propose the Online-Greedy algorithm, which iteratively tries to assign workers who can cover more skills with less cost to a task until the task can be completed. We finally demonstrate the effectiveness and efficiency of our solutions via experiments conducted on both synthetic and real datasets.