  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.
