LAUSR

Most of the research on the incentive mechanism design in crowdsourcing has focused on how to allocate sensing tasks to participants to maximize the social welfare. However, none of them consider the coverage holes created by the uneven distribution of participants. As a result, most of the participants in some popular areas compete for tasks, while many tasks in unpopular areas cannot be completed due to the lack of participants. In this paper, we design a movement-based incentive mechanism for crowdsourcing, where participants are stimulated to move to the unpopular areas and complete the sensing tasks in these areas, which benefits both participants and the platform. We formulate a task allocation problem considering controlled mobility. Since the task allocation problem is NP-hard, we propose a greedy algorithm to solve it and design a critical payment policy to guarantee that participants declare their cost truthfully. Theoretical analysis shows that our proposed incentive mechanism satisfies the desired properties of truthfulness, individual rationality, platform profitability, and computational efficiency. Evaluation results show that the proposed movement-based incentive mechanism outperforms the existing solution under various conditions.