A Truthful Auction for Graph Job Allocation in Vehicular Cloud-assisted Networks

Vehicular cloud computing has emerged as a promising solution to fulfill users' demands on processing computation-intensive applications in modern driving environments. Such applications are commonly represented by graphs consisting of components and edges. However, encouraging vehicles to share resources poses significant challenges owing to users' selfishness. In this paper, an auction-based graph job allocation problem is studied in vehicular cloud-assisted networks considering resource reutilization. Our goal is to map each buyer (component) to a feasible seller (virtual machine) while maximizing the buyers' utility-of-service, which concerns the execution time and commission cost. First, we formulate the auction-based graph job allocation as an integer programming (IP) problem. Then, a Vickrey-Clarke-Groves based payment rule is proposed which satisfies the desired economical properties, truthfulness and individual rationality. We face two challenges: 1) the above-mentioned IP problem is NP-hard; 2) one constraint associated with the IP problem poses addressing the subgraph isomorphism problem. Thus, obtaining the optimal solution is practically infeasible in large-scale networks. Motivated by which, we develop a structure-preserved matching algorithm by maximizing the utility-of-service-gain, and the corresponding payment rule which offers economical properties and low computation complexity. Extensive simulations demonstrate that the proposed algorithm outperforms the benchmark methods considering various problem sizes.Comment: 14 pages, 8 figure

Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles

Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning that can be implemented in the IoV. However, the performance of the FL suffers from the failure of communication links and missing nodes, especially when continuous exchanges of model parameters are required. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the IoV components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources to provide services for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework to solve the allocation of UAV coalitions to groups of IoV components. Specifically, the coalition formation game is formulated to maximize the sum of individual profits of the UAVs. The joint auction-coalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied to solve the allocation of UAV coalitions. The auction scheme is designed to take into account the preferences of IoV components over heterogeneous UAVs. The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profit-maximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer to support the IoV components independently and not to form any coalition