Long-Term Profit-Maximizing Incentive for Crowd Sensing in Mobile Social Networks (1309.7735v5)

Abstract: Crowd sensing is a new paradigm that leverages pervasive sensor-equipped mobile devices to provide sensing services like forensic analysis, documenting public spaces, and collaboratively constructing statistical models. Extensive user participation is indispensable for achieving good service quality. Nowadays, most of existing mechanisms focus on guaranteeing good service quality based on instantaneous extensive user participation for crowd sensing applications. Little attention has been dedicated to maximizing long-term service quality for crowd sensing applications due to their asymmetric interests, preferences, selfish behaviors, etc. To fill these gaps, in this paper, we derive the closed expression of the marginal sensing data quality based on the monopoly aggregation in economics. Furthermore, we design marginalquality based incentive mechanisms for long-term crowd sensing applications, not only to enhance extensive user participation by maximizing the expected total profits of mobile users, but also to stimulate mobile users to produce high-quality contents by applying the marginal quality. Finally, simulation results show that our mechanisms outperform the existing solutions.