HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing
This addresses task allocation inefficiencies in Mobile Crowd Sensing, offering a hybrid approach that is incremental over existing methods.
The paper tackles the challenge of task allocation in Mobile Crowd Sensing by integrating opportunistic and participatory modes into a two-phased hybrid framework called HyTasker, which jointly optimizes both phases under a budget constraint, resulting in more completed tasks under the same budget as shown in experiments on a real-world dataset.
Task allocation is a major challenge in Mobile Crowd Sensing (MCS). While previous task allocation approaches follow either the opportunistic or participatory mode, this paper proposes to integrate these two complementary modes in a two-phased hybrid framework called HyTasker. In the offline phase, a group of workers (called opportunistic workers) are selected, and they complete MCS tasks during their daily routines (i.e., opportunistic mode). In the online phase, we assign another set of workers (called participatory workers) and require them to move specifically to perform tasks that are not completed by the opportunistic workers (i.e., participatory mode). Instead of considering these two phases separately, HyTasker jointly optimizes them with a total incentive budget constraint. In particular, when selecting opportunistic workers in the offline phase of HyTasker, we propose a novel algorithm that simultaneously considers the predicted task assignment for the participatory workers, in which the density and mobility of participatory workers are taken into account. Experiments on a real-world mobility dataset demonstrate that HyTasker outperforms other methods with more completed tasks under the same budget constraint.