UAV based data communication using Wireless Sensor Networks
Bibliographic record
Abstract
This paper studies various data collection and transmission technologies and the architectures concerning Wireless Sensor Networks (WSN) with the aid of Unmanned Aerial Vehicles (UAV). The basic communication methodologies between the sensors and the sensor to the UAVs and UAVs to the destination are studied in detail. The architectures that can be used to perform these operations seamlessly and efficiently are explored. The concept of maximizing the efficiency of UAV and WSN by considering numerous impacting factors is studied and a throughput maximization strategy is explored. Different UAV routing mechanisms to decrease the loss in packet data during transmission are considered and an extensive study on this topic is presented. The energy consumption and overall efficiency of the UAV based communication model have been studied in a comparative perspective weighing down the pros and cons of one over the other. An opportunistic communication algorithm gives a more dynamic outlook to the UAV routing problem and also enhances the efficiency of the overall system. Finally, the identified problems in the previous related work are listed and an alternative solution is proposed.
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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 | 0.000 |
| 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 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".