An Online Fairness-Aware Task Planning Approach for Spatial Crowdsourcing

Abstract

Spatial crowdsourcing, a human-centric paradigm for performing spatial tasks, has drawn rising attention. Task assignment and worker scheduling are basic issues in spatial crowdsourcing, which has a high demand on the timeliness of tasks and fairness among workers. In this paper, we propose Utility-Fairness Index to evaluate the performance of the crowdsourcing platform and introduce the Fairness-Aware Task Planning problem that maximizes the utility while well-maintaining fairness during task assignment. We prove that its offline version is NP-hard, and clarify that there is no deterministic online algorithm with a constant competitive ratio for it. Two greedy-based online algorithms, Traversal Search and Advanced Nearest Neighbor are designed to solve the problem. We make optimization on running time with recurrence method to reach linear complexity, and use Gaussian Mixture Model 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.