Data-Driven Distributionally Robust Optimization for Energy-Efficient Offloading in UAV-Satellite Edge Computing Networks

Jiachen Sun, Xu Chen, Jiawei Wang, Huanxi Cui, Haoge Jia, Sheng Wu

Abstract: The importance of UAV-satellite edge computing networks in disaster relief and scientific exploration has become increasingly prominent, attracting significant attention from both industry and academia. However, under a pre-planned task execution model, fluctuations in data volume often lead to inefficient offloading strategies, significantly increasing the energy consumption risk for UAV-satellite edge computing networks and, in extreme cases, resulting in system failure. Existing offloading approaches either disregard data volume uncertainty, adopt overly conservative robust optimization, or rely on unrealistic distribution assumptions, all of which limit their practicality. To address these limitations, we propose a historical data-driven distributionally robust optimization offloading scheme. Specifically, we first formulate an optimization problem to minimize the total energy consumption and leverage distributionally robust duality theory to derive a tractable formulation. Subsequently, we design an iterative solving algorithm based on the block gradient descent and successive convex approximation methods. Numerical simulations validate that our proposed scheme achieves lower system energy consumption compared to benchmark schemes.

External IDs:dblp:conf/iwcmc/SunCWCJW25