Bibliographic record
Abstract
Ad-hoc networks, characterized by highly dynamic multi hop wireless cormectivity, offer challenges related to unique issues of congestion, channel error, routing instability and network partitioning. Dealing with these issues requires precise detection of network states, which we accomplished by measuring appropriate metrics, such as packet out of order, inter-arrival delay differences, connection throughput, round trip time etc. We evaluated the performance of TCP under variety of network conditions running two important routing protocols namely Dynamic Source Routing (DSR) and Dynamic Sequential Distance Vector (DSDV) routing. These protocols belong to different class of routing protocols. DSR is an on-demand whereas DSDV is a link-state routing protocol. In this project, we carried out detailed simulations of a sizable ad-hoc network using NS2 to study the dynamics of the two routing protocols related to the performance of TCP by calculating the above metrics. We observed that congestion in ad-hoc network exhibits dynamic behavior and sometime it is not as bad as in case of fixed networks. For example we observed that node mobility introduces transience to congestion by dissipating congestion at bottleneck nodes. We observed in at least one scenario that node movement totally avoids congestion. We evaluated the performance under channel error conditions by measuring packets out of order and packet losses for both protocols. We also studied the routing characteristics of both protocols under identical mobility conditions. Finally, we evaluated the worst-case performance under extreme network condition by combining congestion, channel error and node mobility.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".