P2P Overlay Performance in Large-Scale MANETs
Bibliographic record
Abstract
We explored how to deploy P2P overlays in ultimately large-scale Mobile Ad-Hoc Networks (MANETs). We therefore studied the performance of P2P overlays such as Chord, creating a number of flat and hierarchical MANET networks. The hierarchical network consists of clusters, interconnected by a backbone. The subnetworks (cluster or the whole network) ran OLSR as network-layer routing protocol. Each cluster had a gateway, interconnected through a backbone that deployed flooding. As we increased the number of clusters, we kept the number of nodes in the Chord overlay constant. Using simulations in OMNeT++, we evaluated the P2P performance. Our results show that an unmodified P2P network does not perform well even for relatively small network sizes. The performance can be improved through the use of a cross-layered P2P solution, such as OneHopOverlay4MANET. However, such cross-layered approaches require complete information about overlay nodes from the routing layer and are therefore not suitable in hierarchical MANETs. For hierarchical underlays, the performance of the P2P overlay deteriorated as we increased the number of clusters. One of the main reasons is that the backbone quickly became a performance bottleneck.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".