  Mobile crowdsensing (MCS) is a promising sensing paradigm that leverages the
diverse embedded sensors in massive mobile devices. A key objective in MCS is
to efficiently schedule mobile users to perform multiple sensing tasks. Prior
work mainly focused on interactions between the task-layer and the user-layer,
without considering tasks' similar data requirements and users' heterogeneous
sensing capabilities. In this work, we propose a three-layer data-centric MCS
model by introducing a new data-layer between tasks and users, enable different
tasks to reuse the same data items, hence effectively leverage both task
similarities and user heterogeneities. We formulate a joint task selection and
user scheduling problem based on the new framework, aiming at maximizing social
welfare. We first analyze the centralized optimization problem with the
statistical information of tasks and users, and show the bound of the social
welfare gain due to data reuse. Then we consider the two-sided information
asymmetry of selfish task-owners and users, and propose a decentralized market
mechanism for achieving the centralized social optimality. In particular,
considering the NP-hardness of the optimization, we propose a truthful
two-sided randomized auction mechanism that ensures computational efficiency
and a close-to-optimal performance. Simulations verify the effectiveness of our
proposed model and mechanism.
