Energy-Efficient Data Dissemination Using a UAV: An Ant Colony Approach
Why this work is in the frame
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Bibliographic record
Abstract
In this letter, we address the problem of minimizing the energy consumption of disseminating a library of files to a set of Internet-of-Things (IoT) devices using an unmanned aerial vehicle (UAV). A framework is provided, in which a subset of devices are selected to receive data from a UAV and then forward the required data to other devices. Furthermore, optimal energy-efficient path selection is considered in order to realize efficient data dissemination. Specifically, an optimization problem is formulated to minimize the energy expenditure of the IoT devices and UAV while the latter tours to disseminate the required files to the former. An ant colony optimization (ACO) algorithm is developed to solve the optimization problem. Simulation results show that the proposed framework is more energy-efficient compared to a baseline approach, where the UAV hovers above each device to deliver the data. Results also illustrate that the proposed ACO algorithm provides performance close to the optimal solution, which is obtained through exhaustive search.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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