Existing studies in mobile crowdsourcing (aka spatial crowdsourcing), a hot research area in recent years, face the problem of lacking realworld datasets. We thus published a synthetic dataset generator for producing common datasets for mobile crowdsourcing.
The toolbox can generate synthetic workload patterns based on the spatial (location) and temporal (time) distributions of workers and tasks. As shown in the figure below, it also takes into account the various real-world constraints, such as worker region and worker capacity, worker activeness and temporal workload.
The author proposes an interesting and novel paradigm called the HyperLocal Spatial Crowdsourcing, which does not require workers to physically travel to the task locations. The proposed paradigm is more realistic, and the collected data is more trustworthy compared with the assumptions adopted in existing works [1,2,3,4]. In the Figure below, worker A is eligible to report data for both tasks, represented by two circles.
We study how to maximize task coverage under budget constraints in the presence of dynamic arrival of tasks and workers in location-aware crowdsourcing. The goal of the paper is to maximize the number of assigned tasks where a given number of workers can be selected over a time period, under a budget constraint. We consider dynamic cases where the number of tasks and workers are not known a priori. Two problem variants are investigated: one with a given budget for each time period, and the other with a given budget for the entire campaign.