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Record W2122476743 · doi:10.1109/mobiquitous.2005.18

Content-based routing in mobile ad hoc networks

2005· article· en· W2122476743 on OpenAlexaff
Milenko Petrovic, Vinod Muthusamy, Hans‐Arno Jacobsen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComputer networkFault toleranceDistributed computingReliability (semiconductor)Communication sourceOverhead (engineering)

Abstract

fetched live from OpenAlex

The publish/subscribe model of communication provides sender/receiver decoupling and selective information dissemination that is appropriate for mobile environments characterized by scarce resources and a lack of fixed infrastructure. We propose and evaluate three content-based routing protocols: CBR is an adaptation of existing distributed publish/subscribe protocols for wired networks, FT-CBR extends CBR to provide fault-tolerance, and RAFT-CBR provides both fault-tolerance and reliability. Using network simulations we analyze the applicability and test the tradeoffs of these algorithms. We show that RAFT-CBR can guarantee 100% delivery to small groups, at the expense of transmission delay. CBR, with a low message overhead and low delay, is more suitable for larger groups at the expense of reliability. FT-CBR provides comparable delivery rates to RAFT-CBR, as well as low delay, at the expense of increased message cost.

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)0.003
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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

Citations45
Published2005
Admission routes1
Has abstractyes

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