Bibliographic record
Abstract
Advances in wireless technology and mobile computing have provided a major impetus towards development of P2P network over MANET. Both peer-to-peer overlays and mobile ad-hoc network share the key characteristics of self-organizing and decentralization; both need to solve same fundamental problem, that is, how to route queries or packets efficiently. Congestion affecting the performance of P2P overlay on wireless ad-hoc network occurs not only due to buffer overflow but also due to error prone nature of the wireless medium used and the mobility of users. When paths of multiple downloading sessions intersect with each other, they mutually cause congestion for each other that result in packet drops. We identified that P2P MANET suffers query performance primarily due to significant congestion during download session. The unawareness of congestion in P2P MANET system may lead to the longer user response time, high overhead and many packet losses. In order to alleviate the congestion problem, we propose a modification to RAON, CANON, which includes congestion awareness for improving the RAON performance. We evaluated our system through simulation, which shows that CAON improves the performance of query success rate, query delay, packet losses significantly. We also evaluated the impact of mobility model on our system by simulating under both Random way point mobility model and Manhattan mobility model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".