Joint Decision‐Making for UAV Deployment and Computational Offloading Optimized for Energy Consumption and Latency in Space‐Air‐Ground Integrated Networks

ABSTRACT With the rapid advancement of communication technologies, space‐air‐ground integrated networks (SAGIN) have become a pivotal research frontier in current and future communication domains. To tackle critical challenges in SAGIN scenarios, such as excessive task‐related energy consumption and insufficient communication‐computing resources, this paper proposes a three‐tier edge computing architecture integrating satellites, unmanned aerial vehicle (UAV) swarms, and ground systems. Aiming to minimize the system's weighted energy consumption and latency, we investigate the joint optimization of task allocation, user‐UAV association, UAV deployment, and resource allocation between UAVs and low‐earth orbit (LEO) satellites. Formulated as a non‐convex mixed‐integer nonlinear combinatorial optimization problem, this work integrates the branch‐and‐bound method, multi‐start global optimization, and gray wolf optimization (GWO) to develop a suboptimal solution based on block coordinate descent (BCD), which decouples the original problem into three subproblems for independent solving and iterative approximation of the optimal solution. Experimental results show that the proposed algorithm reduces the total system cost by 7.81%, 11.99%, and 45.69% compared with baseline algorithms with random user‐UAV association, random UAV positioning, and random task assignment, respectively, effectively cutting down overall energy consumption and task latency.