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Record W2024633838 · doi:10.1109/itc.2014.6932964

Energy efficiency and traffic offloading in WLANs with caching and mesh capabilities

2014· article· en· W2024633838 on OpenAlexaff
Rosario G. Garroppo, Gianfranco Nencioni, Gregorio Procissi, Luca Tavanti, Bernard Gendron

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsUniversité de Montréal
FundersMinistero dell'Università e della Ricerca
KeywordsComputer scienceWireless mesh networkComputer networkEfficient energy useQuality of serviceInteger programmingLinear programmingMesh networkingEnergy (signal processing)Wireless networkService (business)WirelessTelecommunicationsAlgorithm

Abstract

fetched live from OpenAlex

In this paper we study a wireless access network based on the WLAN technology and enriched with features such as caching and mesh networking. This system is analysed in terms of energy efficiency and traffic offloading, two objectives that are somewhat in contrast, but both relevant to network and service providers as they directly impact the operational cost. To this aim, we developed a mathematical model of the system and solved it to optimality by means of integer linear programming. We can thus show how much saving can be achieved both in terms of energy and traffic, also considering various tradeoff points among the two contrasting objectives. A basic degree of quality of service is also accounted for.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.175
Teacher spread0.169 · 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 teacher head, 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

Citations1
Published2014
Admission routes1
Has abstractyes

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