Intelligent Gaming for Mobile Crowd-Sensing Participants to Acquire Trustworthy Big Data in the Internet of Things

In mobile crowd-sensing systems, the value of crowd-sensed big data can be increased by
incentivizing the users appropriately. Since data acquisition is participatory, crowd-sensing systems face the
challenge of data trustworthiness and truthfulness assurance in the presence of adversaries whose motivation
can be either manipulating sensed data or collaborating unfaithfully with the motivation of maximizing their
income. This paper proposes a game theoretic methodology to ensure trustworthiness in user recruitment in
mobile crowd-sensing systems. The proposed methodology is a platform-centric framework that consists of
three phases: user recruitment, collaborative decision making on trust scores, and badge rewarding. In the
proposed framework, users are incentivized by running sub-game perfect equilibrium and gami cation
techniques. Through simulations, we showthat approximately 50% and a minimum of 15% improvement can
be achieved by the proposed methodology in terms of platform and user utility, respectively, when compared
with fully distributed and user-centric trustworthy crowd-sensing.