Approach Toward Simulation Modeling With Reinforced Offloading for Heterogeneous IoT Terminals Under a Novel STECN
Bibliographic record
Abstract
Satellite terrestrial edge computing network (STECN) has significant potential application deploying mobile edge computing (MEC) in offloading the computational tasks by the heterogeneous Internet of Thing (IoT) terminals under terrestrial network (TN). In this article, we present a methodological approach toward modeling the simulation environment for a novel multitier multiconstellation STECN where the IoT user equipment (UE) from both aviation space and ground will offload computing tasks into the edge satellite network for processing. We designed a network model, a communication and coverage time model and a computing model under the proposed STECN. We proposed two algorithms with three offloading schemes addressing variability in latency tolerance by heterogeneous UEs. We optimized the allocation of communication and computing resources by the satellites under the model by adopting a deep deterministic policy gradient (DDPG) algorithm with an actor-critic network for training and learning. We approached simulation modeling by designing and developing relevant modules, simulation architecture, and workflow. We incorporated techniques to fine tune the training system under specific evaluation matrices so that our simulation model can be followed by others in the domain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".