Unknown Worker Recruitment With Long-Term Incentive in Mobile Crowdsensing

Abstract

Many mobile crowdsensing applications require efficient recruitment of workers whose qualities are often unknown a priori. While prior research has explored multi-armed bandit-based mechanisms with short-term incentives to address this unknown worker recruitment challenge, these mechanisms mostly neglect the enduring participation issues stemming from privacy concern and selection starvation in the long-term task. Therefore, in this paper, we focus on incentivizing long-term participation of unknown workers, thereby providing crucial assurance for crowdsensing applications. We first establish an auction framework based on shuffle differential privacy (SDP), where we leverage SDP’s privacy amplification effect to mitigate privacy-related utility loss when dealing with the privacy-sensitive worker and the utility-sensitive platform. Following this, we model the selection requirements of workers as fairness constraints and propose two novel fairness-aware incentive mechanisms, GFA and IFA, to ensure group and individual fairness for unknown workers, respectively. Theoretical analyses highlight the desirable properties of GFA and IFA, complemented by an in-depth exploration of fairness violation and regret. Finally, numerical simulations are conducted on two real-world datasets, validating the superior performance of the proposed mechanisms.