Fair and Efficient Resource Allocation in UAV-Enhanced Edge Computing: A Dual-Layer Auction Approach

Abstract

With the rapid advancement of Uncrewed Aerial Vehicle (UAV) technology, fleets are increasingly deployed in disaster relief, logistics, and environmental monitoring, necessitating real-time task execution and efficient UAV resource allocation. However, UAVs’ constrained computational and communication capabilities, particularly in dynamic and resource-limited environments, pose significant challenges to effective resource management. To address these issues, we propose a dual-layer auction mechanism within a UAV-enhanced edge-computing architecture to achieve fair and efficient resource allocation. The mechanism integrates an improved double-Dutch auction for dynamic initial resource matching and a Vickrey auction for reallocating unmatched resources, ensuring optimal utilization and fairness. Furthermore, we incorporate the Proximal Policy Optimization reinforcement learning algorithm to adaptively refine resource pricing and auction strategies, and the Gale-Shapley algorithm to optimize UAV-edge-server matching, enhancing allocation flexibility and fairness. Extensive experiments validate the effectiveness of our approach, demonstrating up to 28.7% improvement in social welfare, enhanced resource utilization, and reduced communication costs, particularly in large-scale markets. These findings highlight the potential of the proposed mechanism in optimizing UAV-edge resource management and advancing real-time UAV applications.