BTV-CMAB: A Bi-Directional Trust Verification-Based Combinatorial Multiarmed Bandit Scheme for Mobile Crowdsourcing

Mobile crowdsourcing (MCS) is an emerging paradigm that harnesses the collective power of the crowd to tackle large-scale tasks. To ensure the high-quality worker selection, various combinatorial multiarmed bandit (CMAB)-based schemes have been proposed. However, previous schemes often overlook critical issues. First, the post-unknown worker recruitment (PUWR) problem emerges when the quality of a worker remains unknown despite reported worker data. Second, the presence of Sybil Requesters is often neglected, who manipulate ratings to deceive workers for malicious purposes. To tackle these challenges, we present an innovative scheme called bi-directional trust verification-based CMAB (BTV-CMAB). First, we propose a truth quality discovery approach that effectively addresses the PUWR problem by estimating worker quality. Additionally, we employ a BTV mechanism to assess the Degree of Trust (DoT) of requesters and the reputation of workers. To select top-notch workers for MCS, we combine the worker quality and reputation into an upper confidence bound (UCB) index. The effectiveness of the BTV-CMAB scheme is supported by theoretical proof, which demonstrates its ability to ensure truthfulness and individual rationality. Furthermore, experimental results reveal promising improvements achieved by our scheme, including a 17.44%, increase in the platform's revenue and a significant decrease in regret of up to 88.26%. To the best of our knowledge, this study is the first to propose utilizing a BTV mechanism to effectively address the PUWR problem and counter the threat of Sybil attacks in the CMAB-based worker recruitment process.