  Crowd sensing is a new paradigm which leverages the pervasive smartphones to
efficiently collect and upload sensing data, enabling numerous novel
applications. To achieve good service quality for a crowd sensing application,
incentive mechanisms are necessary for attracting more user participation. Most
of existing mechanisms apply only for the budget-constraint scenario where the
platform (the crowd sensing organizer) has a budget limit. On the contrary, we
focus on a different scenario where the platform has a service limit. Based on
the offline and online auction model, we consider a general problem: users
submit their private profiles to the platform, and the platform aims at
selecting a subset of users before a specified deadline for minimizing the
total payment while a specific service can be completed. Specially, we design
offline and online service-constraint incentive mechanisms for the case where
the value function of selected users is monotone submodular. The mechanisms are
individual rationality, task feasibility, computational efficiency,
truthfulness, consumer sovereignty, constant frugality, and also performs well
in practice. Finally, we use extensive simulations to demonstrate the
theoretical properties of our mechanisms.
