Delivery analysis of multicasting in BitTorrent enabled ad hoc network (MBEAN) routing
Bibliographic record
Abstract
Routing in mobile ad hoc networks is one of the network layer problems and has been researched for quite some time. Yet there is still a need for newer routing protocols as new applications emerge. BitTorrent Enabled Ad Hoc Network (BEAN) routing was proposed for applications such as a network formed by people in a conference hall, people waiting to board a flight and spectators in a ball park. BEAN is motivated by the BitTorrent protocol used by peer-to-peer networks to share large files among a group of peers. The goal of this paper is to study the performance of Multicasting in BitTorrent Enabled Ad Hoc Network (MBEAN) routing in terms of packet delivery through simulations. We compare our results with improved Multicasting in Ad Hoc On Demand Distance Vector (MAODV) routing protocol with prediction of link breakage enabled [7]. MAODV has been tested for a configuration that is long and narrow (rectangular simulation area) resembling a highway condition which does not exactly simulate the network of our interest. We show how MBEAN compares with MAODV for a simulation area that has a more regular geometry. The results show that MBEAN has a better performance than MAODV with prediction for static and mobile networks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".