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Record W3201022335 · doi:10.32920/ryerson.14656281.v1

QoS support using SWAN model in mobile ad-hoc networks

2021· preprint· en· W3201022335 on OpenAlexaff
Ning Zhang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMobile ad hoc networkQuality of serviceComputer networkComputer scienceMobile QoSAdaptive quality of service multi-hop routingWireless ad hoc networkDistributed computingRouting protocolRouting (electronic design automation)Optimized Link State Routing ProtocolService (business)Service providerTelecommunicationsWireless

Abstract

fetched live from OpenAlex

Mobile Ad-Hoc Network (MANET) is a collection of mobile nodes, dynamically forming a temporary network without pre-existing network infrastructure or centralized administration. Due to the bandwidth constraint and dynamic topology of MANETs, supporting quality of Service (QoS) in MANETs is a challenging task. MANETs have certain unique characteristics that pose several difficulties in provisioning QoS. Most routing protocols for MANETs are designed without explicitly considering QoS of the routes. QoS-aware routing requires to find a route that satisfies the end-to-end QoS requirement. QoS in MANETs is a rapidly growing area of research interest. In this report, the challenges of QoS support for MANETs are discussed first.Then the current research on QoS support in MANETs is reviewed, followed by extensive discussion and analysis of QoS models and QoS routing. Finally, one of the QoS models - SWAN is studied to provide a qualitative assessment of the applicability of the model.

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)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.276
Teacher spread0.246 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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