Budget-feasible truthful mechanism for resource allocation and pricing in vehicle computing

Abstract

Vehicle computing holds great potential to provide computation, communication, sensing, and data storage. To accelerate the application of vehicle computing, we address the problem of resource allocation and pricing in vehicle computing (PAPVC), in which the vehicle, as a resource provider, provides heterogeneous resources to serve users through access points or the internet. In our model, each user has multiple tasks of different values under budget constraints, and vehicles have different costs. We formulate the RAPVC problem in a reverse auction-based setting, where a user is regarded as an auctioneer, and the vehicles are regarded as the participants in the auction. However, the vehicle providers are self-interested, and they can submit untrue declarations to maximize the utility. Thus, we aim to design a truthfulness mechanism to maximize group efficiency. We propose a greedy mechanism to solve the PAPVC problem. We then show the proposed mechanism is truthful; that is, our proposed mechanism drives the system into an equilibrium. In addition, we show the proposed mechanism achieves individual rationality, consumer sovereignty, budget feasibility, and computation efficiency. Furthermore, we analyze the approximation ratio of the proposed approximation algorithm. Simulation results indicate that the average percent gap between solutions obtained by the proposed mechanism and optimal solutions is 22%.