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Record W2164254685 · doi:10.1109/twc.2008.080573

Statistical QoS routing for IEEE 802.11 multihop ad hoc networks

2009· article· en· W2164254685 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 Transactions on Wireless Communications · 2009
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer networkComputer scienceWireless ad hoc networkOptimized Link State Routing ProtocolAd hoc wireless distribution serviceWireless Routing ProtocolAdaptive quality of service multi-hop routingDistributed computingDynamic Source RoutingVehicular ad hoc networkDestination-Sequenced Distance Vector routingRouting protocolRouting (electronic design automation)WirelessTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we propose a model-based quality-of-service (QoS) routing scheme for IEEE 802.11 ad hoc networks. Unlike most of QoS routing schemes in the literature, the proposed scheme provides stochastic end-to-end delay guarantees, instead of average delay guarantees, to delay-sensitive bursty traffic sources. Via a cross-layer design approach, the scheme selects the routes based on a geographical on-demand ad hoc routing protocol and checks the availability of network resources by using traffic source and link-layer channel modeling, taking into consideration the IEEE 802.11 characteristics and node interactions. Our scheme extends the well developed effective bandwidth theory and its dual effective capacity concept to multihop IEEE 802.11 ad hoc networks. Extensive computer simulations demonstrate that the proposed scheme is effective in satisfying the end-to-end delay bound to a probabilistic limit.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.312
Teacher spread0.272 · 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