Session-specific Energy Consumption Minimization for UAV-enabled Sensor Data Collection
Bibliographic record
Abstract
With its low cost and high implementation flexibility, unmanned aerial vehicle (UAV) serves as an attractive solution for data collection from remote sensors. UAV can also power the transmission of resource-limited sensors through wireless energy transfer. In this article, we study the UAV energy consumption minimization during an individual sensor charging and data collection session. Specifically, considering the operation of the UAV at a particular sensor, we derive the closed-form expressions of optimal transmission parameters with/without latency constraint. We also analyze the probability of data collection failure due to poor channel condition. The validity of our theoretical analysis is verified by comparing with the results from exhaustive search. According to these results, optimal duration for sensor data transmission should be as short as possible for negligible circuit power, and latency constraint does not have significant impacts on minimum energy consumption for nonnegligible circuit power and channel gain.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".