  With the rapid development of connecting massive devices to the Internet,
especially for remote areas without cellular network infrastructures,
space-air-ground integrated networks (SAGINs) emerge and offload
computation-intensive tasks. In this paper, we consider a SAGIN, where multiple
low-earth-orbit (LEO) satellites providing connections to the cloud server, an
unmanned aerial vehicle (UAV), and nearby base stations (BSs) providing edge
computing services are included. The UAV flies along a fixed trajectory to
collect tasks generated by Internet of Things (IoT) devices, and forwards these
tasks to a BS or the cloud server for further processing. To facilitate
efficient processing, the UAV needs to decide where to offload as well as the
proportion of offloaded tasks. However, in practice, due to the variability of
environment and actual demand, the amount of arrival tasks is uncertain. If the
deterministic optimization is utilized to develop offloading strategy,
unnecessary system overhead or higher task drop rate may occur, which severely
damages the system robustness. To address this issue, we characterize the
uncertainty with a data-driven approach, and formulate a distributionally
robust optimization problem to minimize the expected energy-constrained system
latency under the worst-case probability distribution. Furthermore, the
distributionally robust latency optimization algorithm is proposed to reach the
suboptimal solution. Finally, we perform simulations on the realworld data set,
and compare with other benchmark schemes to verify the efficiency and
robustness of our proposed algorithm.
