Robust Task Scheduling for Delay-Aware IoT Applications in Civil Aircraft-Augmented SAGIN
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".