A dynamic-trust-based recruitment framework for mobile crowd sensing

Abstract

Mobile crowd sensing (MCS) arises as an appealing paradigm, which utilizes participants to contribute sensing data generated from sensors embedded in smart devices in the internet of things (IoT) for the people-centric service delivery and crowd intelligence extraction. Due to the inherent selfishness of human and network's openness, the quality of the data submitted by the participants is not always satisfying. To cope with this problem, a dynamic-trust-based recruitment framework (DTRF) for MCS system is proposed to recruit suitable participants who are trustworthy and always submit high-quality sensing data on time. In this model, we first give the definition of trust, and evaluate the overall trust degree of the participant from multi-dimensional trust evaluation factors: direct trust, feedback trust and incentive function. Then we develop an adaptive weight allocation approach based on information entropy theory, and the algorithm realization is given. Extensive simulations verifies that DTRF can achieve good performance in terms of trustworthy participants selection and task completion rate, compared with trust without feedback model.