Location-Dependent Task Bundling for Mobile Crowdsensing

Abstract

The mobile crowdsensing (MCS) is an emerging sensing paradigm based on the mobile device. For location-dependent sensing tasks (LDSTs), when tasks are farther with low payment from workers, they can be difficult to complete. The completion rate of this unpopular task has always been an issue. Most existing researches mainly focus on how to increase payment for unpopular tasks, but the platform may suffer from it, because an incorrect increase results in an inability to raise the number of completed tasks. In this paper, we present a task bundling reorganized mechanism (TBRM) to improve the platform utility of MCS system. In the proposed mechanism, the unpopular and popular tasks are properly bundled to improve the platform utility. To decrease searching time for suitable bundles, two sub-policies are respectively utilized to design TBRM based on reinforcement learning: the area selection policy and the rule selection policy. Experimental results demonstrate that TBRM outperforms the three benchmark mechanisms, which reveals that TBRM can effectively bundle unpopular tasks and improve platform utility.