Stackelberg Game-Based Task Offloading for Joint Service Caching and Resource Allocation Optimization in UAV-Assisted VEC

Abstract

The development of novel applications causes increased demands on the computational capabilities of Vehicular Edge Computing (VEC). Current works have introduced Unmanned Aerial Vehicles (UAVs) into VEC to solve the resource-constrained problem. However, given the limited storage of UAVs, the key question is to design the offloading strategy and determine which service programs should be cached. In this article, we propose a three-stage game model that aims at providing a precise analysis of the interaction among the Base Station (BS), the UAV, and the User Vehicle (UV). In stage I, the BS is responsible for determining the cache strategy of the UAV and communicating the price strategy to the UVs. In stage II, the UAV communicates the price strategy to the UVs. In stage III, each UV determines its offloading decision based on the price strategy, to minimize the task execution delay and cost. Compared with current approaches, we cache the frequently requested services in the UAV to satisfy the real-time requirements and use game theory to solve the decision-making, which achieves the effect of reducing the delay and cost. The experiment results are performed to assess the convergence and effectiveness of the proposed algorithm.