Motivating Smartphone Collaboration in Data Acquisition and Distributed Computing

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.