UAV-LEO Integrated Backbone: A Ubiquitous Data Collection Approach for B5G Internet of Remote Things Networks
Bibliographic record
Abstract
With the advance of unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites, the integration of space, air and ground networks has become a potential solution to the beyond fifth generation (B5G) Internet of remote things (IoRT) networks. However, due to the network heterogeneity and the high mobility of UAVs and LEOs, how to design an efficient UAV-LEO integrated data collection scheme without infrastructure support is very challenging. In this paper, we investigate the resource allocation problem for a two-hop uplink UAV-LEO integrated data collection for the B5G IoRT networks, where numerous UAVs gather data from IoT devices and transmit the IoT data to LEO satellites. In order to maximize the data gathering efficiency in the IoT-UAV data gathering process, we study the bandwidth allocation of IoT devices and the 3-dimensional (3D) trajectory design of UAVs. In the UAV-LEO data transmission process, we jointly optimize the transmit powers of UAVs and the selections of LEO satellites for the total uploaded data amount and the energy consumption of UAVs. Considering the relay role and the cache capacity limitations of UAVs, we merge the optimizations of IoT-UAV data gathering and UAV-LEO data transmission into an integrated optimization problem, which is solved with the aid of the successive convex approximation (SCA) and the block coordinate descent (BCD) techniques. Simulation results demonstrate that the proposed scheme achieves better performance than the benchmark algorithms in terms of both energy consumption and total upload data amount.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".