Fair Incentive Mechanism for Mobile Crowdsensing

In this chapter, we jointly address practical issues in the incentive mechanism for MCS to fairly incentivize high-quality users’ participation, like (1) the platform has no knowledge about users’ sensing qualities beforehand due to their private information. (2) The platform needs users’ continuous participation in the long run, which results in fairness requirements. (3) It is also crucial to protect users’ privacy due to the potential privacy leakage concerns (e.g., sensing qualities) after completing tasks. Particularly, we propose the three-stage Stackelberg-based incentive mechanism for the platform to recruit participants. In detail, we leverage combinatorial volatile multi-armed bandits (CVMAB) to elicit unknown users’ sensing qualities. We use the drift-plus-penalty (DPP) technique in Lyapunov optimization to handle the fairness requirements. We blur the quality feedback with tunable Laplacian noise such that the incentive mechanism protects locally differential privacy (LDP). Finally, we carry out experiments to evaluate our incentive mechanism. The numerical results show that our incentive mechanism achieves sublinear regret performance to learn unknown quality with fairness and privacy guarantee.