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Record W2117021508 · doi:10.1109/dnsr.2004.1344736

Intelligent ants for adaptive network routing

2004· article· en· W2117021508 on OpenAlexafffund
Hu Yun, A. Nur Zincir‐Heywood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Security and Intrusion Detection
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceRouting tableStatic routingDynamic Source RoutingPolicy-based routingComputer networkNetwork packetLink-state routing protocolRouting (electronic design automation)RouterEnhanced Interior Gateway Routing ProtocolDestination-Sequenced Distance Vector routingEqual-cost multi-path routingMultipath routingDistributed computingGeographic routingRouting protocol

Abstract

fetched live from OpenAlex

An adaptive routing algorithm based on the AntNet algorithm is designed and implemented with a new routing table formation scheme. This addresses the unrealistic requirement of the original AntNet algorithm for global information. The new algorithm requires limited routing information in the routing and traffic statistics tables. The routers only have the most popular destinations in their routing tables and update these destinations at a scheduled time. Each router keeps a traffic table to record visiting data packets. Under this approach, a data packet is forwarded randomly if its destination does not exist in the routing table. Unlike the original AntNet algorithm, ants and data packets have time flags to avoid them having infinite lives. Experiments show that the new approach gives promising results.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.029
GPT teacher head0.252
Teacher spread0.223 · 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

Citations15
Published2004
Admission routes2
Has abstractyes

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