Several works related to spatial crowdsourcing have been proposed in the direction where the task executers are to perform the tasks within the stipulated deadlines. Though the deadlines are set, it may be a practical scenario that majority of the task executers submit the tasks as late as possible. This situation where the task executers may delay their task submission is termed as procrastination in behavioural economics. In many applications, these late submission of tasks may be problematic for task providers. So here, the participating agents (both task providers and task executers) are articulated with the procrastination issue. In literature, how to prevent this procrastination within the deadline is not addressed in spatial crowdsourcing scenario. However, in a bipartite graph setting one procrastination aware scheduling is proposed but balanced job (task and job will synonymously be used) distribution in different slots (also termed as schedules) is not considered there. In this paper, a procrastination aware scheduling of jobs is proliferated by proposing an (randomized) algorithm in spatial crowdsourcing scenario. Our algorithm ensures that balancing of jobs in different schedules are maintained. Our scheme is compared with the existing algorithm through extensive simulation and in terms of balancing effect, our proposed algorithm outperforms the existing one. Analytically it is shown that our proposed algorithm maintains the balanced distribution.

翻译：已有诸多空间众包相关研究提出了任务执行者在规定截止日期内完成任务的方向。尽管设定了截止日期，实际场景中多数任务执行者可能尽可能晚地提交任务。这种延迟任务提交的行为在行为经济学中被称为拖延。在许多应用中，任务的延迟提交可能给任务提供者带来问题。因此，本文阐述了参与代理（包括任务提供者和任务执行者）面临的拖延问题。现有文献中，如何在空间众包场景下在截止日期内防止这种拖延尚未得到解决。然而，在二部图设置中，已提出了一种感知拖延的调度方法，但未考虑不同时间段（也称为调度）中任务（任务与作业将同义使用）的均衡分配。本文通过在空间众包场景中提出一种（随机化）算法，推广了感知拖延的任务调度方案。该算法确保了不同调度中任务的均衡性。通过大量仿真实验与现有算法相比，就均衡效果而言，本文算法优于现有算法。分析表明，本文算法保持了均衡分配。