Parallel Algorithm on GPU for Wireless Sensor Data Collection using Multiple UAVs
Bibliographic record
Abstract
This paper proposes a framework for the wireless sensor data collection using multiple unmanned aerial vehicles (UAVs). Wireless sensors can be used in a wide range of applications to detect information about their environment. Typically limited in power, they have a short transmission range. This paper proposes the use of UAVs as mobile sink nodes to visit the wireless sensors and download their data. The proposed framework calculates location of download points (DP) using an iterative k-means clustering algorithm, computes optimal paths between DPs using a single-source-shortest-path (SSSP) algorithm parallelized on a GPU and use a genetic algorithm to allocate the DPs to the UAVs and finds the order in which the DPs are visited in order to minimize the overall time of the mission. The proposed framework is tested on two maps using 70 and 100 sensors and the parallel implementation on GPU of the SSSP allows for a speedup of 39.4x compared to a sequential execution on CPU.
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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".