IoT-equipped UAV communications with seamless vertical handover
Bibliographic record
Abstract
With advancement in the technology and services in different application areas, UAVs have become a strong market share. Applications range from goods delivery, agriculture, surveillance, mining, industrial inspection, disaster management etc. Furthermore, this vast usability of UAVs can be powered by Internet of Things (IoT) and it can offer a new IoT value-added services. UAVs can be referred to as “things in motion” which can be controlled over the Internet and push the sensor data they collect to the cloud services. But this imposes major challenges with high a volume of data transmissions in some applications. Another area of concern is the choice of mode of communication in LOS (Line of Sight) and BLOS (Beyond Line of Sight) where Wi-Fi could have limited accessibility and other modes like satellite communication have higher cost of data transmissions. In this paper, a RESTful approach to connect UAVs with IoT to stream sensor data to cloud services is presented, providing an efficient solution for data management and transmissions by efficiently gathering, filtering and transmitting data on demand to the cloud services. An efficient vertical handover mechanism is also presented between different modes of communication like Wi-Fi and satellite for BLOS communication challenges to increase reliability or reduce cost.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".