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Record W1995517322 · doi:10.1155/2015/539048

BandEst: Measurement-Based Available Bandwidth Estimation and Flow Admission Control Algorithm for Ad Hoc IEEE 802.15.4-Based Wireless Multimedia Networks

2015· article· en· W1995517322 on OpenAlexaff
Muhammad Omer Farooq, Thomas Kunz

Bibliographic record

VenueInternational Journal of Distributed Sensor Networks · 2015
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceWireless ad hoc networkComputer networkAd hoc wireless distribution serviceBandwidth (computing)Admission controlVehicular ad hoc networkOptimized Link State Routing ProtocolAlgorithmIEEE 802.11Mobile ad hoc networkWirelessWireless networkTelecommunicationsQuality of serviceNetwork packet

Abstract

fetched live from OpenAlex

We highlight different important factors that must be considered for an effective available-bandwidth-based flow admission control algorithm in ad hoc wireless networks. Moreover, we present BandEst; it is a combination of a measurement-based available bandwidth estimation technique and a flow admission control algorithm for ad hoc IEEE 802.15.4-based ad hoc networks that considers the identified factors. Extensive simulations are performed to compare BandEst with the state-of-the-art available-bandwidth-based flow admission control algorithms for ad hoc wireless networks. Our simulation results demonstrate that BandEst significantly outperforms the state-of-the-art available-bandwidth-based flow admission control algorithms for ad hoc wireless 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.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.002
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 head0.022
GPT teacher head0.251
Teacher spread0.228 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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