Although 5G networks have enabled mobile users to get a better experience, task scheduling remains challenging for massive Internet of Things (IoT) devices in remote areas. This paper investigates the task scheduling problem for delay-aware IoT applications in civil aircraft-augmented space-air-ground integrated networks (CAA-SAGIN), where the normalized sky access platforms (SAPs) can collect and forward the terrestrial tasks. Specifically, we first propose an access control scheme for a non-preemptive priority queuing system and a transmission control scheme with cross-layer optimization. Secondly, considering the uncertain distribution of the transmission numbers and generated data, we formulate a robust two-stage stochastic optimization problem of delay minimization. With the proposed robust task scheduling with risk aversion (RTS-RA) algorithm, the original problem can be decomposed into two subproblems, which can be further transformed into tractable semi-definite program (SDP) problems respectively. Simulation results show that the cross-layer optimization scheme can achieve a good tradeoff between delay and throughput. Also, the RTS-RA algorithm outperforms the exiting offloading schemes in terms of end-to-end delay, transmitted data, and energy consumption with lower computational complexity.

中文翻译：

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民用飞机增强型 SAGIN 中延迟感知物联网应用的鲁棒任务调度


尽管5G网络让移动用户获得了更好的体验，但对于偏远地区的海量物联网设备来说，任务调度仍然具有挑战性。本文研究了民用飞机增强空地一体化网络（CAA-SAGIN）中延迟感知物联网应用的任务调度问题，其中标准化天空接入平台（SAP）可以收集和转发地面任务。具体来说，我们首先提出了一种用于非抢占式优先级排队系统的访问控制方案和一种具有跨层优化的传输控制方案。其次，考虑到传输数量和生成数据的不确定分布，我们提出了延迟最小化的鲁棒两阶段随机优化问题。利用所提出的带有风险规避的鲁棒任务调度（RTS-RA）算法，原始问题可以分解为两个子问题，这两个子问题可以分别进一步转化为易处理的半定程序（SDP）问题。仿真结果表明，跨层优化方案能够在延迟和吞吐量之间实现良好的折衷。此外，RTS-RA算法在端到端延迟、传输数据和能耗方面优于现有的卸载方案，并且计算复杂度较低。