Towards a Truthful Online Auction for Cooperative Mobile Task Execution

Although the capabilities of mobile devices have been significantly improved, various resource-hungry mobile applications, such as face recognition, interactive games, and augmented reality, continue to emerge, which creates a tension between mobile applications and mobile devices. Cooperative mobile task execution, in which a mobile device offloads its computation tasks to be executed on the neighboring mobile devices, offers a promising architecture to ease the tension. In this paper, we propose a truthful online auction mechanism for cooperative mobile task execution to allocate computation tasks to adjacent mobile devices dynamically and charge the owner of the tasks appropriately. Specifically, we first model the auction design problem of cooperative mobile task execution as a social welfare maximization problem and prove it is NP-hard. To solve the problem, we then leverage the primal-dual technique to devise an online auction algorithm that makes task allocation decisions and computes the corresponding payments in polynomial time. Theoretical analysis proves that the proposed online auction algorithm achieves the desired properties, including individual rationality, truthfulness, and computational tractability. Moreover, we derive the competitive ratio upper bound of the online approximation algorithm. Extensive simulations based on generated stochastic mobile task and mobile device patterns demonstrate the efficacy of the proposed online auction mechanism.