UAV-Assisted Emergency Communications in Social IoT: A Dynamic Hypergraph Coloring Approach
Bibliographic record
Abstract
In this article, we address the social-awareness property and unmanned-aerial-vehicle (UAV)-assisted information diffusion in emergency scenarios, where UAVs can disseminate alert messages to a set of terrestrial users within their coverage, and then these users can continuously disseminate the received data packets to their socially connected users in a device-to-device (D2D) multicast manner. In this regard, we have to solve both the dynamic cluster formation and spectrum sharing problems in stochastic environments, since both UAVs and terrestrial users may arrive or depart suddenly. For the cluster formation problem, considering that the data rate of a multicast cluster is determined by the member with the worst link condition, we formulate it as a many-to-one matching game and adopt the rotation-swap algorithm to maximize the expected number of users receiving the alerting messages in each time slot. For the dynamic spectrum sharing problem, aiming at eliminating the interference while minimizing the channel switching cost, we propose a dynamic hypergraph coloring approach to model the cumulative interference and maintain the mutual interference at a low level by exploring a small number of vertices, when the graph is dynamically updated, i.e., the insertion/deletion of vertex/edge. Moreover, we prove some crucial properties, including global stability, convergence, and complexity. Finally, simulation results show that our proposed approach can achieve a better tradeoff among the information diffusion speed, channel switch cost, and complexity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 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".