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Record W146461228

On the Robustness of Simple Indoor MANET Simulation Models.

2007· article· en· W146461228 on OpenAlexaff
H. Andrés Lagar-Cavilla, G.S. Baron, Thomas E. Hart, Lionel Litty, Eyal de Lara

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceRobustness (evolution)Mobility modelRouting protocolSoundnessComputer networkSimple (philosophy)Node (physics)Mobile ad hoc networkRadio propagationRadio propagation modelRouting (electronic design automation)Telecommunications
DOInot available

Abstract

fetched live from OpenAlex

Abstract — We show that simple radio propagation and node mobility models widely used in MANET evaluation are not robust in indoor environments. Robust simulation models let researchers extrapolate simulation results and reach reliable conclusions about expected protocol performance. We experiment with two representative MANET routing protocols under different mobility and radio propagation models with decreasing levels of complexity. We show that the effects of successive simplifications to the node mobility and radio propagation models are not consistent across protocols. Moreover, even within the same protocol, the effects on performance can vary erratically as simulation parameters change. Our results raise troubling questions about the soundness of evaluations of MANET routing protocols based on simple radio propagation and node mobility models. I.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.098
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.259
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2007
Admission routes1
Has abstractyes

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