Stackelberg Game with Zero-Determinant Strategy for Incentive Mechanism Design in Socially Aware Mobile Crowdsensing

In Mobile Crowdsensing (MCS), incentive mechanisms are crucial for encouraging mobile users to join tasks while users selfishly pursue personal benefit maximization. While most existing studies focus on the interaction between the requester and users, the internal value of socially aware user relationships remains underexplored. Users naturally form social connections, assisting or collaborating on tasks, but current mechanisms often neglect asymmetric social effects, which can lead to unequal willingness to cooperate and eventual breakdowns in collaboration (e.g., less profitable users refusing to cooperate). To end this, we propose an integrated incentive mechanism that models the interaction between the requester and users as a two-stage Stackelberg game (SG) while accounting for pairwise asymmetric social effects. Pairwise cooperation is governed by the Iterated Prisoner's Dilemma (IPD), with users employing zero-determinant (ZD) strategies to ensure cooperation despite unequal payoffs. Additionally, a plug-and-play sub-algorithm is introduced to filter low-quality or malicious users simultaneously and evaluate task redundancy, enhancing system robustness. We rigorously prove the existence of the Nash equilibrium, design the efficient iterative algorithm for our proposed mechanism, and validate its effectiveness through extensive experiments on real-world social datasets, which demonstrate that our method significantly improves system utility and cooperation stability while ensuring quality of service (QoS) requirements.