LAUSR

With the rich sensing ability and extensive usage of various sensors, mobile crowdsensing (MCS) has become a new paradigm to collect sensing data for various sensing applications. In the modern urban environment, the multisource sensing information and the difference of mobile users make the sensing scenario more and more complex. To improve the applicability of different sensing scenarios, it is necessary to design a heterogeneous user recruitment mechanism for multiple heterogeneous tasks. However, most of the prior works focus on the recruitment of single-type users for homogeneous tasks without considering the heterogeneity of tasks (e.g., spatiotemporal characteristics, sensor requirements, etc.) and users (e.g., personal preferences, carrying sensors, etc). In this article, we propose the problem of heterogeneous user recruitment of multiple heterogeneous tasks (HURoTs) in MCS, with the goal of minimizing the total platform payment and maximizing the task coverage ratio. The HURoT problem is proved to be NP-hard, which is divided into multiple subproblems in different sensing cycles. Moreover, by introducing the user’s utility function, we propose three greedy-based user recruitment algorithms to obtain near-optimal solutions. Extensive experiments are conducted to validate the effectiveness of the proposed schemes.