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Record W1769053037 · doi:10.1109/hotos.1999.798379

Transparent communication management in wireless networks

2003· article· en· W1769053037 on OpenAlexaff
David Kidston, James P. Black, Thomas Kunz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Waterloo
FundersThailand Toray Science Foundation
KeywordsComputer scienceComputer networkQuality of serviceWirelessProxy (statistics)Network packetDistributed computingWireless networkTelecommunications

Abstract

fetched live from OpenAlex

Wireless networks are characterized by the generally low quality of service (QoS) that they provide. In the face of user mobility between heterogeneous networks, distributed applications designed for the higher and less variable QoS of wired networks have difficulty operating. Proxy systems provide one solution to this problem. By placing an intermediary on the communication path between wired and wireless hosts, communication streams can be manipulated for transmission using the available QoS. However, some applications, can or will not be modified for use with a proxy due to a lack of code or resources. This position paper proposes a new proxy model in which services can be applied to communication streams without interfering with the operation of the associated application or communication protocols. A proxy architecture which supports autonomous and third party control of filters is presented, and a new class of filters that allow transparent modification of packet data is illustrated with a transparency-support filter for TCP.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.016
GPT teacher head0.229
Teacher spread0.214 · 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

Citations5
Published2003
Admission routes1
Has abstractyes

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