Joint UAV Position Optimization and Resource Scheduling in Space-Air-Ground Integrated Networks With Mixed Cloud-Edge Computing

Space-aerial-assisted computation offloading has been recognized as a promising technique to provide ubiquitous computing services for remote Internet of Things (IoT) applications, such as forest fire monitoring and disaster rescue. This article considers a space-aerial-assisted mixed cloud-edge computing framework, where the flying unmanned aerial vehicles (UAVs) provide IoT devices with low-delay edge computing service and satellites provide ubiquitous access to cloud computing. We aim to minimize the maximum computation delay among IoT devices with the joint scheduling for association control, computation task allocation, transmission power and bandwidth allocation, UAV computation resource, and deployment position optimization. Through exploiting block coordinate descent and successive convex approximation, we develop an alternating optimization algorithm with guaranteed convergence, to solve the formulated problem. Extensive simulation results are provided to demonstrate the remarkable delay reduction of the proposed scheme than existing benchmark methods.