Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address this challenge, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes, especially under heavy system workloads.

翻译：无人机（UAV）因其快速部署、灵活机动和低成本特性，在协助灾后快速响应救援中发挥着日益重要的作用。然而，无人机面临电池容量和计算资源有限的挑战，这可能导致执行关键任务时缩短预期续航时间并增加救援响应延迟。针对这一挑战，我们首先利用移动边缘计算（MEC）和车辆雾计算（VFC）的空中-地面边缘能力，提出了一种三层灾后救援计算架构，包括车辆雾层、无人机客户端层和无人机边缘层。此外，我们构建了一个联合任务卸载与资源分配优化问题（JTRAOP），旨在最大化时间平均系统效用。由于所构建的JTRAOP被证明是NP-hard问题，我们提出了一种MEC-VFC辅助的任务卸载与资源分配（MVTORA）方法，其中包含基于博弈论的任务卸载决策算法、基于凸优化的MEC资源分配算法，以及基于进化计算的混合VFC资源分配算法。仿真结果表明，与其他基准方案相比，所提方法在系统重负载情况下尤其能实现优越的系统性能。