THUS: A Two-Phase Cross-Platform Hybrid User Recruitment Strategy in Mobile Crowdsensing

In recent years, the mobile crowdsensing (MCS) paradigm has enabled a diverse array of emerging sensing applications by harnessing the collective efforts of ubiquitous mobile users, who collaborate to carry out specific sensing tasks using smart devices. However, the majority of existing works concentrate on a single MCS platform, which struggles to accommodate diverse service requirements. Moreover, these existing researches either consider opportunistic users (OUs) or participatory users (PUs) for task execution, which leads to low task coverage or high recruitment costs, while reducing the sensing quality of tasks. Therefore, in this paper, we introduce a multi-platform scenario where OUs and PUs are combined to complement each other. Then, we formulate a multi-platform hybrid user recruitment (MPHUR) problem within the limited platform budget and user time budget and decompose it into two NP-hard subproblems. To maximize the total sensing quality of tasks, we propose a Two-phase cross-platform Hybrid User recruitment Strategy called THUS. In the first phase, we present a greedy-based opportunistic user recruitment algorithm to match the user-task pair iteratively with maximum sensing quality according to the shortage degree of PUs. In the second phase, the MCS platforms assign PUs to complete the tasks that OUs fail to cover based on their residual budget. We propose a multi-task minimum-cost flow algorithm to recruit PUs for the remaining tasks. The extensive experiments are conducted on two real-world datasets to demonstrate the effectiveness of our proposed THUS.