Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints

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 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 as well as their co-location relationship. We study two problem variants in this paper: budget is constrained for every timestamp, i.e. fixed, and budget is constrained for the entire campaign, i.e. dynamic. For each variant, we study the complexity of its offline version and then propose several heuristics for the online version which exploit the spatial and temporal knowledge acquired over time. Extensive experiments with real-world and synthetic datasets show the effectiveness and efficiency of our proposed solutions.