Energy-Efficient Spatially-Correlated Data Aggregation Using Unmanned Aerial Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the problem of minimizing the energy consumption of data gathering from a set of Internet-of-things (IoT) devices using an unmanned aerial vehicle (UAV). The spatial correlation among the data of the IoT devices is considered. A framework is provided, in which a subset of devices are selected to contribute, and the optimal path that the UAV should follow, along with the aggregation points at which the UAV stops and aggregates the data in an energy-efficient fashion is also considered. In this framework, an optimization problem is formulated to minimize the energy expenditure of the IoT devices and UAV while the latter tours to aggregate the required information from the former. A solution based on a greedy algorithm is provided, in which the optimization problem is decomposed into two complementary sub-problems. The first sub-problem selects the contributing devices using a genetic algorithm. The second sub-problem optimizes the locations of the data aggregation points and assigns the active devices to each aggregation point. Simulation results show that the proposed framework can save significant energy.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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