Modeling epidemic data diffusion for wireless mobile networks
Bibliographic record
Abstract
ABSTRACT Data/content dissemination among the mobile devices is the fundamental building block for all the applications in wireless mobile collaborative computing, known as mobile peer‐to‐peer. Different parameters such as node density, scheduling among neighboring nodes, mobility pattern, and node speed have a tremendous impact on data diffusion in a mobile peer‐to‐peer environment. In this paper, we develop analytical models for object diffusion time/delay in a wireless mobile network to apprehend the complex interrelationship among these different parameters. In the analysis, we calculate the probabilities of transmitting a single object from one node to multiple nodes using the epidemic model of spread of disease. We also incorporate the impact of node mobility, radio range, and node density in the networks into the analysis. Utilizing these transition probabilities, we estimate the expected delay for diffusing an object to the entire network both for single object and multiple object scenarios. We then calculate the transmission probabilities of multiple objects among the nodes in the wireless mobile network considering network dynamics. Through extensive simulations, we demonstrate that the proposed scheme is efficient for data diffusion in the wireless mobile network. Copyright © 2012 John Wiley & Sons, Ltd.
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 | 0.002 | 0.006 |
| 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 | 0.002 | 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".