Assessing the performance of AODV, DYMO, and OLSR routing protocols in the context of larger-scale denser MANETs
Bibliographic record
Abstract
Assessing the performance of mobile ad hoc network (MANET) routing protocols has typically been done within the context of networks with2where 150m to 200m per-node communication ranges are used. In such networks 1 to 2 hop communications dominate. A basic question, therefore, is how do these protocols perform in denser networks where multi-hop communications are innately required? Through the simulation study of a larger-scale denser 360-node multi-hop network, this work shows that, contrary to prior smaller-scale lower density MANET studies, AODV and DYMO outperforms OLSR within multi-hop networks. To the authors' knowledge this deficiency within OLSR has not been previously reported. These issues are of interest as the wide-scale adoption of smartphones have provided a pragmatic deployment platform for such larger-scale denser MANETs, particularly within urban cores and for non-cellular based network services.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".