  Mobile crowdsensing has shown a great potential to address large-scale data
sensing problems by allocating sensing tasks to pervasive mobile users. The
mobile users will participate in a crowdsensing platform if they can receive
satisfactory reward. In this paper, to effectively and efficiently recruit
sufficient number of mobile users, i.e., participants, we investigate an
optimal incentive mechanism of a crowdsensing service provider. We apply a
two-stage Stackelberg game to analyze the participation level of the mobile
users and the optimal incentive mechanism of the crowdsensing service provider
using backward induction. In order to motivate the participants, the incentive
is designed by taking into account the social network effects from the
underlying mobile social domain. For example, in a crowdsensing-based road
traffic information sharing application, a user can get a better and accurate
traffic report if more users join and share their road information. We derive
the analytical expressions for the discriminatory incentive as well as the
uniform incentive mechanisms. To fit into practical scenarios, we further
formulate a Bayesian Stackelberg game with incomplete information to analyze
the interaction between the crowdsensing service provider and mobile users,
where the social structure information (the social network effects) is
uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium
are validated by identifying the best response strategies of the mobile users.
Numerical results corroborate the fact that the network effects tremendously
stimulate higher mobile participation level and greater revenue of the
crowdsensing service provider. In addition, the social structure information
helps the crowdsensing service provider to achieve greater revenue gain.
