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Record W1963484481 · doi:10.1109/cjece.2004.1425808

Perfor mance of a QoS-based multiple-route ad hoc on-demand distance vector protocol for mobile ad hoc networks

2004· article· en· W1963484481 on OpenAlexafffundvenue
Abraham O. Fapojuwo, O. Salazar, A.B. Sesay

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2004
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Standards and TechnologyUniversity of Calgary
KeywordsComputer networkComputer scienceQuality of serviceMobile ad hoc networkOptimized Link State Routing ProtocolBackupAd hoc On-Demand Distance Vector RoutingAdaptive quality of service multi-hop routingAd hoc wireless distribution serviceRouting protocolDistance-vector routing protocolWireless Routing ProtocolWireless ad hoc networkNetwork packetDistributed computingWirelessTelecommunications

Abstract

fetched live from OpenAlex

This paper proposes a quality of service based multiple-route ad hoc on-demand distance vector (QoS-MRAODV) routing protocol for achieving and maintaining QoS in mobile ad hoc networks (MANETs). The QoS-MRAODV protocol supports one active QoS-based primary route and several backup routes to provide hot standby redundancy against frequent route failures that are prevalent in MANETs. Results from extensive performance simulation of the QoS-MRAODV protocol demonstrate that it is indeed a viable protocol for achieving and maintaining QoS support in MANETs, providing the additional benefits of fast route discovery time and low routing overhead without a significant increase in end-to-end packet delay.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations16
Published2004
Admission routes3
Has abstractyes

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