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 two real-world mobility datasets demonstrate that HyTasker outperforms other methods with more completed tasks under the same budget constraint.

中文翻译：

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HyTasker：移动人群感知中的混合任务分配

任务分配是移动人群感知 (MCS) 中的一个主要挑战。虽然以前的任务分配方法遵循机会主义或参与模式，但本文建议将这两种互补模式整合到一个名为 HyTasker 的两阶段混合框架中。在离线阶段，选择一组工人（称为机会工人），他们在日常工作中（即机会​​模式）完成 MCS 任务。在在线阶段，我们分配另一组工人（称为参与式工人），并要求他们专门移动以执行机会式工人未完成的任务（即参与式模式）。HyTasker 不是分别考虑这两个阶段，而是使用总激励预算约束共同优化它们。特别是，在 HyTasker 的离线阶段选择机会主义工人时，我们提出了一种新颖的算法，该算法同时考虑了参与式工人的预测任务分配，其中考虑了参与式工人的密度和流动性。对两个真实世界移动数据集的实验表明，在相同的预算约束下，HyTasker 的性能优于其他方法，完成更多的任务。