Lightweight Group Handover for Uncrewed Aerial Vehicles (UAVs)
Bibliographic record
Abstract
Uncrewed Aerial Vehicles (UAVs), commonly known as drones, are widely deployed and often transmit sensitive data via cellular networks, Wi-Fi, or device-to-device (D2D) communication frameworks. Due to their aerial mobility, UAVs are well-suited for a wide range of applications such as military missions, cargo transport, mapping, and agricultural monitoring. However, securing their communications remains a significant challenge. Their continuous movement and frequent data transmissions make secure authentication difficult to maintain, especially as UAVs often need to switch between different network cells during flight. For example, a drone initially connected to cell A may need to handover to cell B as it progresses along its route. This study is designed to present new authentication and handover processes for multiple nodes within a predefined group. The proposed method makes the group handover process simple and low-cost, while also being resistant to various security threats. Compared to a previous handover scheme based on elliptic curves and Lagrange interpolation, the proposed method, leveraging inner product space, demonstrates significantly improved performance. The corresponding test results are also presented.
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 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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".