Latency-Aware Optimization of UAV Deployment, Computation Offloading, and Resource Allocation for IoRT in Space-Air-Ground Integrated Networks

The unmanned aerial vehicle (UAV)-assisted space-air-ground integrated networks (SAGIN) can provide communication and computing services for Internet of Remote Things (IoRT) in the absence of ground cellular network coverage. In this paper, we propose an edge computing architecture based on SAGIN, comprising three parts: a satellite, UAVs, and ground-based IoRT devices. The satellite is responsible for providing access to cloud computing resources. UAVs are equipped with mobile edge computing (MEC) servers. And IoRT devices generate latency-sensitive tasks but possess limited computing capabilities. Our objective is to minimize task processing delays by jointly optimizing UAV deployment, computation offloading, and time slot resource allocation to meet the increasing demands of IoRT devices. Specifically, the proposed problem is decomposed into two components. First, for the deployment of multiple UAVs, we propose a multi-agent softmax deep double deterministic policy gradient (MASD3) approach, which enables UAVs to adjust their flight trajectories based solely on observed information for adaptive deployment. Second, for the computation offloading and time slot resource allocation problems in SAGIN, we employ a numerical computation-based iterative optimization method to minimize the occupation of time slots by computation offloading. Simulation results demonstrate that our proposed solution significantly reduces overall delay compared to alternative benchmark schemes.