Coverage-Oriented Task Assignment for Mobile Crowdsensing

Crowdsensing tasks are usually described by certain features or attributes, and the task assignment essentially performs a matching with respect to the worker or user's preference on these features. However, the existing matching strategy could lead to a misaligned task coverage problem, i.e., some popular tasks tend to enter workers' candidate task lists, while some less popular tasks could be always unsuccessfully assigned. To ensure task coverage after the assignment, the system may have to increase their biding costs to reassign such tasks, which causes a high operational cost of the crowdsensing system. To address this problem, we propose to migrate certain qualified workers to the less popular tasks for increasing the task coverage and meanwhile, optimize other performance factors. By doing this, other performance factors, such as task acceptance and quality, can be comparably achieved as recent designs, while the system cost can be largely reduced. Following this idea, this article presents cTaskMat, which learns and exploits workers' task preferences to achieve coverage-ensured task assignments. We implement the cTaskMat design and evaluate its performance using both real-world experiments and data set-driven evaluations, also with the comparison with the state-of-the-art designs.