RMDF-CV: A Reliable Multi-Source Data Fusion Scheme With Cross Validation for Quality Service Construction in Mobile Crowd Sensing

Kejia Fan, Jialin Guo, Runsheng Li, Yuanye Li, Anfeng Liu, Jianheng Tang, Tian Wang, Mianxiong Dong, Houbing Song

Abstract: Mobile Crowd Sensing is a prevalent and efficient paradigm for multi-source data collection, where Multi-source Data Fusion (MDF) plays a crucial role in constructing quality data collection services. Current MDF methods often require the majority of participating sensing sources to be credible, or assume that the workers’ credibility is either prior known or easily calculable. However, due to the presence of uncredible environments and the problem of Information Elicitation Without Verification (IEWV), these methods are impractical. It may lead to a vicious cycle where the recruitment of uncredible workers affects the quality of the estimated truth, which can further lead to misjudgments of worker credibility, thereby exacerbating the quality of subsequent recruitment. In this article, a Reliable Multi-source Data Fusion scheme with Cross Validation (RMDF-CV) is proposed to obtain reliable truth for service construction. Specifically, we first introduce the Combinatorial Multi-Armed Bandit (CMAB) model to recruit high-credibility workers by balancing exploration and exploitation. Then, we establish three-stage truth data through three different data sources: Unmanned Aerial Vehicles, credible workers, and Deep Matrix Factorization. Theoretical analyses and extensive simulations confirm the excellent performance of our RMDF-CV scheme.