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Extended WIFI Network Formal Design Model for Ubiquitous Emergency Events

2016· article· en· W4242945637 on OpenAlexaff
Zayan Elkhaled, Hamid Mcheick, Hicham Ajami

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInternet of Things and Social Network Interactions
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsComputer scienceEvent (particle physics)Natural disasterArchitectureComputer securityFocus (optics)Telecommunications networkThe InternetWork (physics)Network architectureInformation exchangeComputer networkTelecommunicationsWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

Telecommunication is the exchange of information and data over significant distance by electronic means. During extreme events such as natural disasters and urgent events it becomes more and more important to preserve the communication devices and infrastructure to exchange information between rescue teams and persons in damaged zone based on their area. When extreme event happens, many communication scenarios can be considered. We focus on a the case of destruction of traditional communication networks during an emergency event such as natural disasters in which it is important to find an alternative network architecture to prevent the death and injury of thousands of people. The rescue teams are unable to locate and communicate with victims on right time. This work presents network architectural design model to extend the range of WIFI networks and help people access to Internet or get rescue when the damage affects the most existing telecommunication networks. This model is validated by analyzing two communication scenarios.

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)0.003
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: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.038
GPT teacher head0.288
Teacher spread0.250 · 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
GenreMethods

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

Citations2
Published2016
Admission routes1
Has abstractyes

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