Enhanced Data Delivery framework for dynamic Information-Centric Networks (ICNs)
Bibliographic record
Abstract
In this paper, we present an Enhanced 2-Phase Data Delivery (E2-PDD) framework for Information-Centric Networks (ICNs), focusing on efficient content access and distribution as opposed to mere communication between data consumers and publishers. We employ an approach of growing eminence, where requests are initiated by consumers seeking particular services that are data-dependent. High-level Controllers (HCs) receive the consumers' requests and issue queries to a multitude of data publishers. The publishers in our topology include a wide variety of ubiquitous nodes that could be either stationary or mobile, operating under different protocols. In order to consider fundamental challenges in ICNs such as node mobility and data disruption, our E2-PDD framework employs Low-level Controllers (LCs) that act as moderators between the HCs and the data publishers, executing data queries for a top tier and replying back with a set of candidate rendezvous points obtained from a bottom tier. The HCs maximize selection based on the nearest rendezvous. Extensive simulation results have been used to evaluate our E2-PDD framework in terms of key performance metrics in ICNs viz., average in-network delay, and publisher load, given different mobility pause time durations and data consumers' densities.
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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 | 0.002 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".