Task Offloading in Multi-Access Edge Computing Enabled UAV-Aided Emergency Response Operations

In emergency response operations, using uncrewed aerial vehicles (UAVs) has recently become a promising solution due to their flexibility and easy deployment. However, tasks performed by the UAVs, e.g., object detection human pose recognition, usually require high computation capacity energy supply. Furthermore, offloading edge server-equipped base stations may not always be possible because of lack infrastructure or distance. Therefore, UAV-aided servers can deployed near UAV scouts provide computing services. perform all types since it limitations on memory, available software, central processing unit (CPU), graphics (GPU) capacity. this study focuses task (TO), power, resource allocation (PRA) problems in multi-layer MEC-enabled network while taking into account CPU GPU requirements tasks, devices (i.e., computational resources, energy), type UAVs perform. The problem is formulated as non-convex mixed-integer nonlinear minimize weighted sum maximum consumption ratio total execution latency ratio, then decomposed converted an integer convex problem. A messy genetic algorithm (mGA)-based TO PRA strategy (mGA-TPR) proposed solve problem, where two strategies are based Karush–Kuhn–Tucker conditions used Simulation results verify that scheme outperform baseline methods.