Reliable Low-Latency Task Offloading and Resource Allocation Method for Space-Air-Ground Integrated Networks
Fei Bu1, Zheng Wang3,*, Yong Pan4, Zhaomin Wu1, Yuchen Liang1, Zhongshan Zhu4, Tengfei Tu5
1 Technology and Platform Department, Beijing Municipal Big Data Center, Beijing, China
2 College of Economics and Management, Beijing University of Technology, Beijing, China
3 Institute of Digital Economy Innovation, Beijing Academy of Science and Technology, Beijing, China
4 Beijing Computing Center Company Ltd., Beijing Academy of Science and Technology, Beijing, China
5 School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China
* Corresponding Author: Zheng Wang. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.083956
Received 14 April 2026; Accepted 11 June 2026; Published online 27 August 2026
Abstract
Space-Air-Ground Integrated Networks (SAGIN) provide a multi-layered, wide-coverage computing infrastructure for distributed urban sensing systems. However, their heterogeneity and dynamics pose unprecedented challenges for task offloading and resource allocation. Existing methods struggle to simultaneously address the complexity of cross-layer decision-making and reliability assurance under uncertain conditions. This paper proposes a novel framework, termed DRL-RA, which synergistically integrates Deep Reinforcement Learning (DRL) with reliability-aware optimization. The framework consists of two complementary components: (1) a Dueling Double Deep Q-Network (D3QN) module that learns adaptive policies to make offloading decisions among various options including local execution, terrestrial edge, UAVs, and satellites; (2) a Reliability-Aware Multi-Objective Optimization Framework (RA-MOOF) that introduces explicit reliability guarantees through cross-layer link reliability modeling, node availability estimation, and smooth reliability proxy functions. Addressing the heterogeneous communication characteristics of the SAGIN architecture, this paper establishes a complete cross-layer delay model and composite reliability metrics. The reliability formulation is defined under explicitly stated conditional-independence assumptions, and the proposed smooth constraint terms are treated as surrogate CMDP costs rather than exact hard chance-constraint guarantees. Extensive experiments in a SAGIN simulation environment demonstrate that the proposed method improves the task completion rate by 3.8%, reduces average latency by 11.1%, and increases system reliability by 3.9% compared to state-of-the-art benchmarks. The optimization-only RA-Opt baseline is used as a non-real-time optimization reference for assessing reliability-aware offloading decision quality, while deployment-time decision-latency comparisons are interpreted primarily among learned inference policies. Comprehensive ablation studies and statistical validation across multiple random seeds confirm the contributions of each component, while cross-layer offloading decision analysis verifies the effectiveness of the method across different network layer selections.
Keywords
Task offloading; resource allocation; deep reinforcement learning; reliability optimization; space-air-ground integrated network; urban sensing; edge computing; multi-objective optimization
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