  Spatial Crowdsourcing (SC) is a novel platform that engages individuals in
the act of collecting various types of spatial data. This method of data
collection can significantly reduce cost and turnover time, and is particularly
useful in urban environmental sensing, where traditional means fail to provide
fine-grained field data. In this study, we introduce hyperlocal spatial
crowdsourcing, where all workers who are located within the spatiotemporal
vicinity of a task are eligible to perform the task, e.g., reporting the
precipitation level at their area and time. In this setting, there is often a
budget constraint, either for every time period or for the entire campaign, on
the number of workers to activate to perform tasks. The challenge is thus to
maximize the number of assigned tasks under the budget constraint, despite the
dynamic arrivals of workers and tasks. We introduce a taxonomy of several
problem variants, such as budget-per-time-period vs. budget-per-campaign and
binary-utility vs. distance-based-utility. We study the hardness of the task
assignment problem in the offline setting and propose online heuristics which
exploits the spatial and temporal knowledge acquired over time. Our experiments
are conducted with spatial crowdsourcing workloads generated by the SCAWG tool
and extensive results show the effectiveness and efficiency of our proposed
solutions.
