The proliferation of advanced mobile terminals opened up a new crowdsourcing
avenue, spatial crowdsourcing, to utilize the crowd potential to perform
real-world tasks. In this work, we study a new type of spatial crowdsourcing,
called time-continuous spatial crowdsourcing (TCSC in short). It supports broad
applications for long-term continuous spatial data acquisition, ranging from
environmental monitoring to traffic surveillance in citizen science and
crowdsourcing projects. However, due to limited budgets and limited
availability of workers in practice, the data collected is often incomplete,
incurring data deficiency problem. To tackle that, in this work, we first
propose an entropy-based quality metric, which captures the joint effects of
incompletion in data acquisition and the imprecision in data interpolation.
Based on that, we investigate quality-aware task assignment methods for both
single- and multi-task scenarios. We show the NP-hardness of the single-task
case, and design polynomial-time algorithms with guaranteed approximation
ratios. We study novel indexing and pruning techniques for further enhancing
the performance in practice. Then, we extend the solution to multi-task
scenarios and devise a parallel framework for speeding up the process of
optimization. We conduct extensive experiments on both real and synthetic
datasets to show the effectiveness of our proposals
