Dynamic Online User Recruitment With (Non-) Submodular Utility in Mobile CrowdSensing

Mobile CrowdSensing (MCS) has recently become a powerful paradigm that recruits users to cooperatively perform various tasks. In many realistic settings, users participate in real time and we have to recruit them in an online manner. The existing works usually formulate the online recruitment problem as a budgeted optimal stopping problem with submodular user utility, while we first argue that not only the budget but also the time constraints can jointly influence the recruitment performance. For example, if we have less budget but plenty of time, we should recruit users with more patience. Second, considering the user’s cooperative willingness, its contribution may be diminishing or even irregular. Hence, we also need to address not only submodular cases but also their non-submodular utility. In this paper, we study the online user recruitment problem with (non-)submodular utility under the budget and time constraints. To deal with the two constraints, we first estimate the number of users to be recruited and then recruit them in segments. Moreover, we extend the segmented strategy with a non-submodular utility, which has the submodularity ratio $\gamma $ and the competitive ratio $\gamma ^{2}(1-e^{-1})/7$ . Furthermore, to correct estimation errors and utilize newly obtained information, we dynamically re-adjust the segmented strategy and also prove that the dynamic strategy achieves a competitive ratio of ${\gamma ^{2}(1-e^{-1})(1-e^{-\gamma /2})/7}$ . Finally, a reverse auction-based online pricing mechanism is lightly built into the proposed user recruitment strategy, which achieves truthfulness and individual rationality. Extensive experiments on three real-world data sets validate the proposed online user recruitment strategy under the (non-) submodular utility and two constraints.

I. Introduction

With the great popularization of mobile devices and wireless communications, Mobile CrowdSensing (MCS) [2] has rapidly become a powerful paradigm, which recruits a number of mobile users to cooperatively perform various urban sensing and computing tasks, e.g., traffic estimation, indoor-outdoor localization, and map semantics identification. In general, the MCS applications should provide proper rewards for the recruited users in order to cover their costs and encourage the participation [3]. However, considering the limited budget, we usually cannot afford all of the users, but have to recruit those who can complete the tasks more effectively, which raises the fundamental user recruitment problem in MCS [4], [5].