Filling the Communication Gaps in Drone Delivery
Bibliographic record
Abstract
The Internet of Drones (IoD) is expected to revolutionize the utilization of drones in urban environments. Drone delivery applications are expected to become commonplace in people's everyday lives. Such applications will establish a network where drones will have organized airspace access, with variations in network node density based on location and time. Within this environment, the IoD network may encounter communication gaps among drones. In scenarios like drone delivery, altering a drone's route solely for message delivery within the network is impractical. We aim to deploy an auxiliary drone network to support IoD applications, similar to using auxiliary networks in vehicular networks for message delivery. This study introduces the AuxIoD method, which utilizes a genetic algorithm to position auxiliary drones strategically, bridging communication gaps within the IoD network. Compared to other strategies, our approach facilitated the delivery of approximately 30% more messages in the evaluated scenario.
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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".