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Unmanned aerial vehicles (UAVs) play an increasingly important role inassisting fast-response post-disaster rescue due to their fast deployment,flexible mobility, and low cost. However, UAVs face the challenges of limitedbattery capacity and computing resources, which could shorten the expectedflight endurance of UAVs and increase the rescue response delay duringperforming mission-critical tasks. To address this challenge, we first presenta three-layer post-disaster rescue computing architecture by leveraging theaerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehiclefog 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 andresource allocation optimization problem (JTRAOP) with the aim of maximizingthe time-average system utility. Since the formulated JTRAOP is proved to beNP-hard, we propose an MEC-VFC-aided task offloading and resource allocation(MVTORA) approach, which consists of a game theoretic algorithm for taskoffloading decision, a convex optimization-based algorithm for MEC resourceallocation, and an evolutionary computation-based hybrid algorithm for VFCresource allocation. Simulation results validate that the proposed approach canachieve superior system performance compared to the other benchmark schemes,especially under heavy system workloads.

Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue