Heterogeneous-belief based incentive schemes for crowd sensing in mobile social networks

Crowd sensing is a new paradigm which exploits pervasive mobile devices to provide complex sensing services in mobile social networks (MSNs). To achieve good service quality for crowd sensing applications, incentive mechanisms are indispensable to attract more participants to guarantee long-term extensive user participation. Most of existing research works apply only for instantaneous sensing data collections, where all participants׳ information are known as a priori. Thus, how to tackle long-term extensive user participation occurring in practical crowd sensing applications with the coverage constraint becomes peculiarly challenging. In this paper, we model the problem as a restless multi-armed bandit process rather than a regular auction, where users submit their sensing data to the platform (the campaign organizer) over time, and the platform chooses a subset of users to collect sensing data. Then, to maximize the social welfare satisfying the coverage constraint for the infinite horizonal continuous sensing, we design incentive schemes based on heterogeneous-belief values for joint social states and realtime throughput. Analysis results indicate that our schemes outperform the best existing solution.