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Record W2052351476 · doi:10.1109/twc.2014.2314643

FW-DAS: Fast Wireless Data Access Scheme in Mobile Networks

2014· article· en· W2052351476 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

VenueIEEE Transactions on Wireless Communications · 2014
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsUniversity of Waterloo
FundersNational Research Foundation of Korea
KeywordsComputer scienceComputer networkWireless distribution systemMobile broadbandData accessWirelessCacheLatency (audio)Wireless networkWireless intrusion prevention systemBase stationWi-Fi arrayTelecommunicationsDatabase

Abstract

fetched live from OpenAlex

In wireless data access applications, reduction of both the access latency and the wireless traffic volume is essential. In this paper, we propose a fast wireless data access scheme (FW-DAS) for wireless data access applications in which data objects are frequently updated and fast access to data objects is indispensable. In FW-DAS, different operation modes are defined depending on the data object popularity, and only popular data objects are proactively pushed to the access point/base station to minimize the access latency while mitigating the traffic load over the wireless link. An analytical model for the access latency is developed and an operation mode selection algorithm is introduced to reduce the access latency. Extensive simulation results show the effects of access-to-update ratio, data popularity, cache size, data object size, and wireless bandwidth. Analytical and simulation results demonstrate that FW-DAS can reduce the access latency with reasonable traffic load compared with poll-each-read (PER)/callback (CB) and their combinations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
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 score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0100.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.313
Teacher spread0.258 · 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