DTRF: A Dynamic-Trust-based Recruitment Framework for Mobile Crowd Sensing Networks

Mobile Crowd Sensing (MCS) is a promising paradigm in which mobile users collect and share sensor data from their local environment using wireless mobile devices. The inherent openness of this platform and the selfishness of individuals make it easy to contribute low-quality sensor data, so the recruitment of suitable participants who are trustable and contribute high-quality sensor data, becomes a fundamental requirement for MCS system. In this paper, we propose a dynamic-trust-based recruitment framework (DTRF) for MCS system. Real-time direct trust and lightweight feedback aggregation trust are combined to select the well-suited participants. In addition, we adopt an adaptive weight allocation approach to calculate the overall trust degree of the participants. Theoretical analysis and extensive simulation confirm that DTRF can efficiently select the trustworthy participants and effectively stimulate the participants to contribute high-quality sensor data and thus get high task completion rate and data quality.