Dependency-Based Task Assignment in Spatial Crowdsourcing

Abstract

Task assignment is one of the central problems in spatial crowdsourcing research. A good assignment approach will match the best performer to the task. Complex tasks account for an increasing proportion of task assignment demands, most of the previous researches on complex task assignment have ignored the dependency relationships between tasks, resulting in many invalid matches and wasting worker resources. A complex task can be assigned only after its dependent task is assigned, such as house decoration. Secondly, task quality is also an important factor to be considered in the task assignment process, the high-quality completion of tasks will benefit all three parties in the crowdsourcing system. Therefore, this paper proposes a dependency-based greedy approach, under the constraints of distance, time, budget, and skills, this approach first assigns a set of available workers to tasks without dependency and maximizes the total quality of assigned tasks. Finally, extensive experiments are conducted on the dataset, and the experimental results proved the effectiveness of the proposed approach in this paper.