On the performance and policies of mobile peer-to-peer network protocols
Bibliographic record
Abstract
Certain "data diversity" approaches have been proposed recently as the means to avoid the demonstrated lack of scalability in mobile ad-hoc networks (MANETs). However, exploiting data diversity requires the development of particular protocols that incite the cooperation of nodes. A feature of the protocols we propose is that, instead of assuming all nodes to be interested in the same data item, we assume different per-node data items of interest and a large population of data items (files) with a popularity that follows a Zipf distribution. We also consider dynamic environments where, over time, new nodes join the network (and others depart). Together with a one-for-one reciprocal exchange policy, we observe the impact of density and per-node initial file sets. Extending the basic idea of adjacent node exchanges to a limited-length path routing, and taking care of proper incentives for nodes to route data on behalf of other nodes, we find an improved throughput for the entire system. However, we find no clear evidence that the per-node perceived throughput for a node's desired files is any different than in the non-cooperative case. Thus, we question whether there exist incentive schemes that could engage nodes to support the data diversity paradigm.
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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 | 0.014 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".