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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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".