Truthful Mechanism for Resource Allocation and Pricing in Vehicle-Assisted Mobile Edge Computing
Xi Liu, Jun Liu, Weidong Li
Abstract
Mobile edge computing (MEC) provides stable and powerful computing services, and vehicle computing provides rich sensing services. Therefore, we develop vehicle-assisted mobile edge computing (VAMEC), integrating MEC and vehicle computing to maximize their advantages and broaden their applications. We address the resource allocation and pricing problem in VAMEC, in which the vehicle, as a resource provider, collaborates with the MEC servers to provide services to users. To enable MEC servers and vehicles to work in cooperation, we propose a mixed auction model that consists of two-step auctions: the first one-way auction and the second double auction. We consider three types of users based on their characteristics, where users participate in different auctions depending on the types. Users and vehicles are self-interested and may submit untrue values to harm the system's profits. Therefore, we propose the overall truthfulness for the mixed auction model, that is users or vehicles that cannot obtain higher utility by declaring untrue values regardless of which auction they participate in. To the best of our knowledge, this is the first study proposing the overall truthfulness and showing how to design the mechanism that achieves it. We then propose an overall truthfulness mechanism and show it achieves individual rationality, budget balance, and consumer sovereignty. Simulation results indicate that the proposed mechanism can increase social welfare by at least 24%, compared with other mechanisms.