Quality-Improved and Delay-Aware Incentive Mechanism for Mobile Crowdsensing With Social Concerns: A Stackelberg Game Approach

With the explosive popularity of mobile devices, mobile crowdsensing (MCS) has emerged as a promising large-scale data collection paradigm. Suitable incentive mechanisms are essential for encouraging user participation. Current MCS work more or less ignores three factors. First, mobile users are assumed to be independent of each other, ignoring social effects. Second, due to the heterogeneity of users, if you just blindly attract users without distinguishing them, the data quality can decrease. Finally, the limited communication resource allocation problem during uploading sensing results is ignored. Therefore, we model the quality-improved and delay-aware incentive mechanism with social concerns as a two-stage Stackelberg game, in which the rational use of social effects not only motivates user participation but also avoids a serious decline in information value due to repetition, and reasonable allocation of communication resources ensures the timeliness of delay-sensitive tasks. Furthermore, data screening, similarity analysis, voting, and reputation are used simultaneously to improve data quality. The Hessian matrix in a multiuser, multitask hyperspace setting is utilized to verify the existence and uniqueness of the game equilibrium. The closed-form expressions of the optimal requester pricing and the optimal user data load strategies are derived, respectively. The proposed mechanism is compared with gather–scatter, incentive-G, Blockchain-based secure, interactive, and fair MCS (BSIF), and Socially-aware incentive mechanism (SAIM) algorithms. Extensive simulation results on a real trajectory dataset show that compared with these state-of-the-art algorithms, the proposed incentive mechanism can motivate users to provide more data loads with few rewards, greatly improve the requester utility, and suppress the data upload of malicious users.