SGCS: A Cost-Effective Quality Control System for Strategic Workers in Mobile Crowd Sensing

Han Wang, Anfeng Liu, Neal N. Xiong

Abstract: Many game-based data collection schemes aim to optimize system profits in Mobile Crowd Sensing (MCS). These schemes often assume that the platform knows the data quality upon receipt from workers, ignoring the verification costs; and assume all low-quality submissions are detected. However, due to the challenge of Information Elicitation Without Verification (IEWV), previous game strategies fail to address two key issues in real-world MCS: (1) Verification incurs costs, so the Nash equilibrium from previous studies may not hold. (2) Cheating workers may not be detected, leading to poor-quality data submissions, a scenario not considered in previous models. To address these challenges, we propose a new Stackelberg Game-based quality Control System (SGCS). Theoretically, we derive the minimum verification rate required for workers to submit high-quality data, considering their strategic responses to the platform's verification rate. We also design a Worker-Dependent Verification Rates (WDVR) algorithm that identifies honest workers focusing on long-term gains, reducing verification rates for them to lower average verification costs and enhance platform utilities. Our approach is validated through a drone-assisted data collection application, demonstrating that: (1) A minimum effective verification rate ensures strategic workers submit high-quality data. (2) There is a complex trade-off between data quality, verification rates, and platform utilities. Higher data quality increases platform income but also raises verification costs more rapidly, potentially reducing overall utilities. The proposed SGCS provides a practical game-theoretic method for MCS data collection.