  This paper analyzes and compares different incentive mechanisms for a master
to motivate the collaboration of smartphone users on both data acquisition and
distributed computing applications. To collect massive sensitive data from
users, we propose a reward-based collaboration mechanism, where the master
announces a total reward to be shared among collaborators, and the
collaboration is successful if there are enough users wanting to collaborate.
We show that if the master knows the users' collaboration costs, then he can
choose to involve only users with the lowest costs. However, without knowing
users' private information, then he needs to offer a larger total reward to
attract enough collaborators. Users will benefit from knowing their costs
before the data acquisition. Perhaps surprisingly, the master may benefit as
the variance of users' cost distribution increases.
  To utilize smartphones' computation resources to solve complex computing
problems, we study how the master can design an optimal contract by specifying
different task-reward combinations for different user types. Under complete
information, we show that the master involves a user type as long as the
master's preference characteristic outweighs that type's unit cost. All
collaborators achieve a zero payoff in this case. If the master does not know
users' private cost information, however, he will conservatively target at a
smaller group of users with small costs, and has to give most benefits to the
collaborators.
