  With the rapid development of mobile devices and the crowdsourcig platforms,
the spatial crowdsourcing has attracted much attention from the database
community, specifically, spatial crowdsourcing refers to sending a
location-based request to workers according to their positions. In this paper,
we consider an important spatial crowdsourcing problem, namely reliable
diversity-based spatial crowdsourcing (RDB-SC), in which spatial tasks (such as
taking videos/photos of a landmark or firework shows, and checking whether or
not parking spaces are available) are time-constrained, and workers are moving
towards some directions. Our RDB-SC problem is to assign workers to spatial
tasks such that the completion reliability and the spatial/temporal diversities
of spatial tasks are maximized. We prove that the RDB-SC problem is NP-hard and
intractable. Thus, we propose three effective approximation approaches,
including greedy, sampling, and divide-and-conquer algorithms. In order to
improve the efficiency, we also design an effective cost-model-based index,
which can dynamically maintain moving workers and spatial tasks with low cost,
and efficiently facilitate the retrieval of RDB-SC answers. Through extensive
experiments, we demonstrate the efficiency and effectiveness of our proposed
approaches over both real and synthetic data sets.
