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Record W1984044090 · doi:10.1109/mcom.2005.1522133

QoS routing for wireless ad hoc networks: problems, algorithms, and protocols

2005· article· en· W1984044090 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Communications Magazine · 2005
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceComputer networkWireless Routing ProtocolAdaptive quality of service multi-hop routingDistributed computingAd hoc wireless distribution serviceDynamic Source RoutingWireless ad hoc networkRouting protocolQuality of serviceOptimized Link State Routing ProtocolLink-state routing protocolZone Routing ProtocolRouting (electronic design automation)WirelessTelecommunications

Abstract

fetched live from OpenAlex

QoS routing plays an important role for providing QoS in wireless ad hoc networks. The goals of QoS routing are in general twofold: selecting routes with satisfied QoS requirement(s), and achieving global efficiency in resource utilization. In this article we first discuss some key design considerations in providing QoS routing support, and present a review of previous work addressing the issue of route selection subject to QoS constraint(s). We then devise an on-demand delay-constrained unicast routing protocol. Various strategies are employed in the protocol to reduce the communication overhead in acquiring cost-effective delay-constrained routes. Simulation results are used to verify our expectation of the high performance of the devised protocol. Finally, we discuss some possible future directions for providing efficient QoS routing support in wireless ad hoc networks.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.956
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.307
Teacher spread0.266 · 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