Abstract
Spatial crowdsourcing has a high demand on timeliness, it requires workers to finish tasks in a proper order, and is reflected by the exponentially reduced utility. Fairness among workers is an important issue to the attraction of the platform. In this paper, we propose Utility-Fairness Index (UFI) to evaluate the performance of the crowdsourcing platform, based on which we address the Fairness-Aware Task Planning (FATP) problem that aims at task planning, such that the utility is maximized while fairness is well maintained. It is different from the previous studies concerning task assignment in which the completion order of the task and the fairness among workers is often ignored. Before digging deeper into the formulated FATP problem, we first prove the NP-hardness of its offline version, then we prove that there is no online algorithm with a constant competitive ratio for it. Two algorithms, Traversal Search (TS) and Advanced Nearest Neighbor (ANN), are proposed to solve this problem. To optimize the planning, we adopt Gaussian Mixture Model (GMM) to reveal the distribution regularity of tasks in these algorithms. Experiments on both synthetic and real datasets validate the efficiency and effectiveness of our algorithms.
Published Version
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