Adaptive $ D$-Hop Connected Dominating Set in Highly Dynamic Flying Ad-Hoc Networks
Bibliographic record
Abstract
By exploring the intelligent cooperation of unmanned aerial vehicle (UAV) swarms, the formed flying ad-hoc networks (FANETs) can support a variety of collaborative operations with real-time communications in emergency scenarios. To reduce the prohibitively high routing overhead with the connectivity guaranteed of multi-hop links, UAV swarms can construct a virtual backbone network (VBN) based on the graph-theoretical$d$-hop connected dominating set ($d$-CDS), where each UAV outside VBN can send collected data to VBN within$d$-hop distance. However, due to the high dynamics of FANETs in emergency scenarios, the optimal solution may not match the current status, which results in frequently intermittent connectivity. Besides, recomputing the solution from scratch will lead to significant maintance costs. Therefore, it is crucial to adapt the minimal$d$-CDS to topology changes. To this end, we propose an$ \mathcal {O}(d\log (N))$-approximation algorithm (i.e.,$ N$denotes the maximal number of nodes) with expected$ \mathcal {\widetilde{O}}(d\Delta ^2)$(i.e.,$ \Delta$denotes the maximal degree of a vertex over the sequence of updates) time per update. The simulation results demonstrate that our adaptive solution can strike a better trade-off among the routing overhead, response time, and maintance costs per topology update compared with state-of-the-art schemes in emergency scenarios.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".