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Record W3011534399 · doi:10.1142/s0219649220400110

Algorithmic Identification of the Best WLAN Protocol and Network Architecture for Internet-Based Applications

2020· article· en· W3011534399 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

VenueJournal of Information & Knowledge Management · 2020
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsLakehead University
Fundersnot available
KeywordsVoice over IPComputer scienceQuality of serviceComputer networkJitterThe InternetFile Transfer ProtocolThroughputNetwork packetService (business)Packet lossSet (abstract data type)WirelessTelecommunicationsWorld Wide Web

Abstract

fetched live from OpenAlex

This research developed a novel algorithm to evaluate internet-based services such as VoIP, Video Conferencing, HTTP and FTP, of different IEEE 802.11 technologies in order to identify the optimum network architecture among Basic Service Set (BSS), Extended Service Set (ESS) and the Independent Basic Service Set (IBSS). The proposed algorithm will yield the rank order of different IEEE 802.11 technologies. By selecting the optimum network architecture and technology, the best overall network performance that provides good voice, video and data quality is guaranteed. Furthermore, it meets the acceptance threshold values for the VoIP, Video Conferencing, HTTP and FTP quality metrics. This algorithm was applied to various room sizes ranging from [Formula: see text][Formula: see text]m to [Formula: see text][Formula: see text]m and the number of nodes ranged from 1 to 65. The spatial distributions considered were circular, uniform and random. The Quality of Service (QoS) metrics used were delay, jitter, throughput and packet loss.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.235

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 science0.0010.000
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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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