  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.
