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Record W1503049715 · doi:10.1109/wimob.2005.1512903

Quality of service for ad hoc on-demand distance vector routing

2006· article· en· W1503049715 on OpenAlexaff
Yihai Zhang, T. Aaron Gulliver

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer networkAd hoc On-Demand Distance Vector RoutingComputer scienceQuality of serviceDistance-vector routing protocolDynamic Source RoutingMobile ad hoc networkOptimized Link State Routing ProtocolDestination-Sequenced Distance Vector routingAdaptive quality of service multi-hop routingRouting protocolWireless ad hoc networkNetwork packetDistributed computingWirelessTelecommunications

Abstract

fetched live from OpenAlex

Quality-of-service (QoS) is a desirable feature for mobile ad hoc networks (MANETs) due to the growth of multimedia applications. However, the mobile nature and dynamic topology of MANETs make it difficult to provide QoS assurance in such networks. In this paper we propose a QoS routing protocol based on AODV (QS-AODV), which creates routes according to application QoS requirements. A local repair mechanism is used to improve the packet delivery ratio. It is shown that QS-AODV provides performance comparable to AODV under light traffic conditions. In heavy traffic, QS-AODV provides higher packet delivery ratios and lower routing overheads, at a cost of slightly longer end-to-end delays. The effects of mobility on performance is also presented.

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)0.004
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.022
GPT teacher head0.276
Teacher spread0.254 · 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

Citations82
Published2006
Admission routes1
Has abstractyes

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