Topology evolution model for ad hoc‐cellular hybrid networks based on complex network theory
Bibliographic record
Abstract
Summary Switching and routing are two important issues in the ad hoc‐cellular hybrid network. Frequently switching between different network modes will lead to the increase of time delay, and unreasonable routing will reduce information transmission efficiency and shorten the network life. In order to reduce the influence of these problems to the network, in this paper, we proposed a robust and efficient ad hoc network topology evolution model, which can reduce the switching probability and establish efficient transmission paths. In this model, the mobile users' residual energy, available channel quality, and the importance are taken into account. The model is based on Barrat, Barthelemy, and Vespignani model and the triad formation mechanism, which ensures the network topology have scale‐free and small‐world features. The topology construction process of the ad hoc network is composed of three parts: (i) new users join the network; (ii) new users establish connections with existing users; (iii) the connections between existing users were deleted because of the network optimization and the interference of cellular network. Simulation results show that the network built by this model with small‐world and scale‐free features has small average shortest path length and the robustness against random nodes failure, which will significantly improve the transmission efficiency and reduce the probability of switching between different transmission model. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| 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".