Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing

Abstract

This paper analyzes and compares different incentive mechanisms for a client to motivate the collaboration of smartphone users on both data acquisition and distributed computing applications. Data acquisition from a large number of users is essential to build a rich database and support emerging location-based services. We propose a reward-based collaboration mechanism, where the client announces a total reward to be shared among collaborators, and the collaboration is successful if there are enough users willing to collaborate. We show that if the client knows the users' collaboration costs, then he can choose to involve only users with the lowest costs by offering a small total reward. However, if the client does not know users' private cost 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. Distributed computing aims to solve computational intensive problems in a distributed and inexpensive fashion. We study how the client can design an optimal contract by specifying different task-reward combinations for different user types. Under complete information, we show that the client will involve a user type as long as the client's preference for that type outweighs the corresponding cost. All collaborators achieve a zero payoff in this case. But if the client does not know users' private cost information, he will conservatively target at a smaller group of efficient users with small costs. He has to give most benefits to the collaborators, and a collaborator's payoff increases in his computing efficiency.