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.
Hien To, Liyue Fan, Luan Tran, and Cyrus Shahabi, Real-Time Task Assignment in Hyperlocal Spatial Crowdsourcing under Budget Constraints, In Proceeding of IEEE International Conference on Pervasive Computing and Communications (PerCom 2016), Sydney, Australia, March 14-18, 2016