Spatial crowdsourcing: An AI-based quality-aware task allocation and procrastination prevention mechanism

Abstract

In a distributed system framework, spatial crowdsourcing (SC) is a highly important area of research where task allocation to task executors (TEs) is an important step. Tasks are requested by a task provider and are allocated by an SC platform to TEs. However, TEs may submit the allocated task as late as possible, known as procrastination. Plenty of research works are available on task allocation in SC, whereas few research works are found that address procrastination. In a bipartite graph setting, a procrastination-aware scheduling is proposed. A recent work uses ChatGPT for procrastinating agents. Balanced distribution of tasks has not been addressed there. Recently, an algorithm was proposed that distributes tasks in a balanced manner in different slots to mitigate procrastination in SC. Here, we propose a quality-aware task allocation mechanism in an SC environment that combines a data science approach with a reinforcement learning-based approach. Once TEs are allocated tasks, we have proposed an AI-enabled (learning-the-variance) algorithm to distribute the tasks into slots with a more balanced distribution than any of the existing algorithms to mitigate procrastination. Our procrastination prevention mechanism outperforms existing methods, which is shown by extensive simulations. Analytically, it is shown that the proposed mechanism maintains a balanced distribution.