Human-in-the-loop Real-time Task Allocation

With the rapid development of intelligent devices and real-time positioning systems, spatial crowdsourcing (SC) platforms, one key component of which is assigning tasks to the appropriate workers in real-time, are thriving. In this paper, we propose the Human-in-the-loop Real-time Task Allocation (HRTA) problem, where the SC platform dynamically pushes the top-k task lists to workers, and workers can confirm tasks in real-time. To address this problem, we propose a top-k initialization algorithm and two index structures based on edge and grid, respectively. Sufficient experiments on two real-world data sets suggest that our proposed algorithms are efficient and effective.