Green Data-Collection From Geo-Distributed IoT Networks Through Low-Earth-Orbit Satellites
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Bibliographic record
Abstract
As a critical supplementary to terrestrial communication networks, low-Earth-orbit (LEO) satellite-based communication networks have been gaining growing attention in recent years. In this paper, we focus on data collection from geo-distributed Internet-of-Things (IoT) networks via LEO satellites. Normally, the power supply in IoT data-gathering gateways is a bottleneck resource that constrains the overall amount of data upload. Thus, the challenge is how to collect the data from IoT gateways through LEO satellites under time-varying uplinks in an energy-efficient way. To address this problem, we first formulate a novel optimization problem, and then propose an online algorithm based on Lyapunov optimization theory to aid green data-upload for geo-distributed IoT networks. The proposed approach is to jointly maximize the overall amount of data uploaded and minimize the energy consumption, while maintaining the queue stability even without the knowledge of arrival data at IoT gateways. We finally evaluate the performance of the proposed algorithm through simulations using both real-world and synthetic data traces. Simulation results demonstrate that the proposed approach can achieve high efficiency on energy consumption and significantly reduce queue backlogs compared with an offline formulation and a greedy “Big-Backlog-First” algorithm.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it