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

Many game-based data collection schemes are proposed 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 costs of verification; and assume that 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, meaning the Nash equilibrium from previous studies may not hold. (2) Workers engaging in cheating may not be caught, encouraging them to risk submitting poor-quality data—an assumption that differs from all previous models. In this paper, we propose a new S tackelberg G ame-based quality C ontrol S ystem (SGCS) to address these challenges. 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. Additionally, we design a Worker-Dependent Verification Rates (WDVR) algorithm that identifies workers who are more honest due to their focus on long-term gains and accordingly reduces verification rates for them, thereby reducing average verification costs and increasing platform utilities. Finally, we validate our approach through a UAV-assisted data collection application, demonstrating that: (1) There exists a minimum effective verification rate required to ensure that strategic workers submit high-quality data. (2) There is a complex trade-off between data quality, verification rates, and platform utilities. Higher data quality increase platform income but also raise verification costs more rapidly, potentially reducing overall utilities. Hence, maximizing platform utilities does not necessarily align with maximizing data quality. The proposed SGCS scheme provides a practical game-theoretic data collection method for MCS.