Incentive Mechanism for Spatial Crowdsourcing With Unknown Social-Aware Workers: A Three-Stage Stackelberg Game Approach

In this paper, we investigate the incentive problem in Spatial Crowdsourcing (SC), where mobile social-aware workers have unknown qualities and can share their answers to tasks via social networks. The objectives are to recruit high-quality workers and maximize all parties' utilities simultaneously. However, most existing works assume that the qualities of workers are known in advance or cannot take all parties' utilities into account together, especially having not considered the impact of social networks. Thus, we propose an incentive mechanism based on the multi-armed bandit and three-stage Stackelberg game, called TACT. We first design a greedy arm-pulling scheme to recruit workers, which not only can solve the exploration-exploitation dilemma but also takes workers' social relations into account. Based on the recruitment results, we further design the utility functions incorporating with social benefits for workers, and model the payment computation problem as a three-stage Stackelberg game among all participants. Next, we derive the optimal strategy group so that each party can maximize its own utility to form a multi-win situation. Moreover, we theoretically prove the unique existence of Stackelberg equilibrium and the worst regret bound. Finally, we conduct extensive simulations on a real trace to corroborate the performance of TACT.

Automated summary

This paper investigates the incentive problem in Spatial Crowdsourcing (SC) with mobile social-aware workers of unknown qualities who can share their answers via social networks. Existing works fail to consider the impact of social networks and cannot maximize all parties' utilities. To address these issues, the authors propose an incentive mechanism called TACT based on the multi-armed bandit and three-stage Stackelberg game. Simulation results demonstrate the effectiveness of TACT.