Incentive-aware Task Location in Spatial Crowdsourcing

Fei Zhu, Shushu Liu, Junhua Fang, An Liu

Abstract: With the popularity of wireless network and mobile devices, spatial crowdsourcing has gained much attention from both academia and industry. One of the critical components in spatial crowdsourcing is task-worker matching, where workers are assigned to tasks to meet some pre-defined objectives. Previous works generally assume that the locations of tasks are known in advance. However, this does not always hold, since in many real world applications where to put tasks is not specific and needs to be determined on the fly. In this paper, we propose Incentive-aware Task Location (ITL), a novel problem in spatial crowdsourcing. Given a location-unspecific task with a fixed budget, the ITL problem seeks multiple locations to place the task and allocates the given budget to each location, such that the number of workers who are willing to participate the task is maximized. We prove that the ITL problem is NP-hard and propose three heuristic methods to solve it, including even clustering, uneven clustering and greedy location methods. Through extensive experiments on a real dataset, we demonstrate the efficiency and effectiveness of the proposed methods.