A random obstacle‐based mobility model for delay‐tolerant networking
Bibliographic record
Abstract
Abstract When evaluating a new protocol in the network, it is important to use a realistic mobility model to reflect the actual performance of a mobile system. Only the realistic mobility model can better mimic the reality and get more reliable data. However, most existing mobile models of delay‐tolerant networking (DTN) move randomly or on the road according to some rules under the environment without obstacles. These mobile models, without considering the impact of obstacles on the node, do not accord with the fact. To address this problem, we propose a random obstacle‐based mobility model (ROM) aimed at better simulating the real trajectory of a human for DTN in the presence of obstacles. In this model, we can place arbitrary‐shape obstacles in accordance with any actual scene, as well as considering the influence of obstacles on the signal. The mobile path of a node calculated by this node is the shortest path to the destination avoiding certain types of obstacles. In addition, the propagation model contains the attenuation of the signal due to the existence of obstacles. As a result, we have developed a complete obstacle mobility model which is more suitable for studying the performance of the network. We augment the ‘opportunistic network environment’ (ONE) simulator of DTN with required extensions and show that characteristics of the DTN are very different using the new model than it is under models that ONE currently provides. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".