Towards Energy-Efficient Data Collection by Unmanned Aerial Vehicle Base Station With NOMA for Emergency Communications in IoT
Bibliographic record
Abstract
For emergency communications in an internet of thing (IoT) network, a large number of gateways are distributed to gather the data traffic. Considering the practical difficulty of deploying multiple territorial base stations (TBSs) in a wide range, unmanned aerial vehicle base station (UAV-BS) can fly to a specific point and hover above there to collect data traffic from gateways. In this paper, we aim to maximize the UAV-BS energy efficiency under the constraints of total serving delay, UAV-BS flying speed, and the maximum available transmitting power of gateways, etc. Firstly, we propose a distributed gateway cluster (GC) algorithm to group gateways into multiple GCs based on the distances among gateways. Next, the UAV-BS flies and hovers above each GC, where the gateways in the GC simultaneously transmit data to the UAV-BS by non-orthogonal multiple access (NOMA). By analyzing the NOMA feature, we propose theorems optimizing the UAV-BS hovering height to minimize the transmitting power of the gateway with the maximum transmitting power among the gateways in a GC. Based on the proposed theorems, we formulate the joint optimization problem to maximize the UAV-BS energy efficiency with only the variables of UAV-BS flying speed and the serving time for each GC. The optimization problem is effectively solved by the geometric programming (GP) method. Finally, we verify the effectiveness of the proposed algorithms by extensive simulation results.
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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) | 0.000 | 0.001 |
| 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 | 0.000 | 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".