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Record W2073871162 · doi:10.1109/icc.2014.6883676

A utility based access point selection method for IEEE 802.11 wireless networks with enhanced quality of experience

2014· article· en· W2073871162 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Guelph
FundersFundação para a Ciência e a TecnologiaInstituto de Telecomunicações
KeywordsRSSComputer scienceSelection (genetic algorithm)Point (geometry)Quality (philosophy)Computer networkHidden node problemWirelessRange (aeronautics)Wireless networkInter-Access Point ProtocolData miningTelecommunicationsMachine learningWi-FiEngineeringWorld Wide WebWi-Fi array

Abstract

fetched live from OpenAlex

Over the past ten years, many solutions have been proposed to address the problem of access point selection in IEEE 802.11 Wi-Fi networks. The standard, which recommends that user devices select an access point based on received signal strength (RSS) has many shortcomings and leads to poor performance. Many of the solutions proposed lead to better performance under some circumstances and with a particular goal in mind. However, in general, each solution has shortcomings as well. In this paper, techniques in access point selection are surveyed dating back to 2002. These approaches are compared and classified, and the problems and limitations are identified. Lastly, a utility-based method which is proposed which is generalized and may take into account a wide range of interests and goals. The performance of the proposed utility-based approach is evaluated with some preliminary simulations in ns3.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.722
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.049
GPT teacher head0.372
Teacher spread0.324 · 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

Quick stats

Citations20
Published2014
Admission routes1
Has abstractyes

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