Data Collection in UAV-Assisted Wireless Sensor Networks Powered by Harvested Energy
Bibliographic record
Abstract
Unmanned aerial vehicles (UAV) assisted-data collection is a new and promising application in many practical scenarios and is gathering a lot of interest. UAVs have the advantage of flexibility as they can be deployed in difficult terrains and hence can be employed for data collection in wireless sensor networks (WSNs). However UAV assisted-data collection in WSN faces many challenges due especially to energy limitations at both the UAV and the sensors. Hence, in this paper, we adress the data collection problem, by considering wireless power transfer (WPT) from the UAV to the sensor nodes (SNs). Our objective is to minimize the UAV total mission time that is defined as the amount of time needed by the UAV to collect all the data available at the SNs and to replenish their energies. To solve this NP-hard problem, which is formulated as an integer linear program, we propose two heuristics, namely the nearest neighbor algorithm (NNA) and the genetic algorithm (GA). In addition to the optimal solution, we also propose two other simple algorithms as benchmarks, in order to demonstrate the efficiency of the proposed algorithms by means of simulations.
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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 | 0.000 | 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 | 0.000 | 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 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".