A method for assessing the performance of multicast algorithms in wireless networks
Bibliographic record
Abstract
In a mobile wireless environment, mobile nodes often form an albitrary and dynamically changing netvr'ork topology.In some mobile networks, such as ad hoc, multicast is often used to support datâ distribution to many receivers by multihop infrastructureless communication.There have been many algorithms and protocols developed for multicast routing in general but so far there is no simple standa¡:d approach for deciding which muìticast algorithrn is the best for a network with changing topologies.It is difficult to establish this standald since these algor'ìthms work in different ways.For this reason, we focus on the maìn feature that multicast algorithms have in common, which is the capability to deal with a dynamic network.A dynamic netwolk can be characterized by its changing topology.In order to study the effect of changing topology on the ability to maintain cornmunication between mobile nodes, we deveÌop a nethod to assess properly any multicast algolithms based on two new pararneters.The two proposed metrics are the average number of arcs per node and the radius of the network topology.Each arc represents ditect reachability between any pair of nodes.The fadius of the network topology is important to measure the movement of nodes within time unit.We present a new approach for using these two parameters to Íeprcsent netwolk cornplexity in a simple way.These parameters are used as direct variables input in assessing multicast algorithms.What we ale investigating is how to use our proposed metrics to measure and see if the ar.rangement of the nodes in the network affects the algorithm performance.We set up an experiment that can be used to measure the quality of the existing, and even the future, r¡ulticast routing algorithns.The expeliment involves the movement of the nodes so that any algolithm can be tested to analyze its robustness towalds different topologies.Our contr.ibution is a new approach for assessing the multicast performance in a networ-k by exploring the characteristics of the network itself.The result is a tool that is very useful to decide whether any nulticast algorithm is good enough to be applied in mobile wi¡eless networks.vlll
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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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".