  Currently, explosive increase of smartphones with powerful built-in sensors
such as GPS, accelerometers, gyroscopes and cameras has made the design of
crowdsensing applications possible, which create a new interface between human
beings and life environment. Until now, various mobile crowdsensing
applications have been designed, where the crowdsourcers can employ mobile
users (MUs) to complete the required sensing tasks. In this paper, emerging
learning-based techniques are leveraged to address crowdsensing game with
demand uncertainties and private information protection of MUs. Firstly, a
novel economic model for mobile crowdsensing is designed, which takes MUs'
resources constraints and demand uncertainties into consideration. Secondly, an
incentive mechanism based on Stackelberg game is provided, where the
sensing-platform (SP) is the leader and the MUs are the followers. Then, the
existence and uniqueness of the Stackelberg Equilibrium (SE) is proven and the
procedure for computing the SE is given. Furthermore, a dynamic incentive
mechanism (DIM) based on deep reinforcement learning (DRL) approach is
investigated without knowing the private information of the MUs. It enables the
SP to learn the optimal pricing strategy directly from game experience without
any prior knowledge about MUs' information. Finally, numerical simulations are
implemented to evaluate the performance and theoretical properties of the
proposed mechanism and approach.
