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Record W2915289913 · doi:10.1109/glocom.2018.8647598

Differentiated QoS to Heterogeneous IoT Nodes in IEEE 802.11ah RAW Mechanism

2018· article· en· W2915289913 on OpenAlexaff
M. Zulfiker Ali, Jelena Mišić, Vojislav B. Mišić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer networkComputer scienceQuality of serviceIEEE 802.1XProtocol (science)Heterogeneous networkAccess controlInternet of ThingsMedia access controlService setThroughputDistributed computingWireless networkIEEE 802.11WirelessWi-FiComputer securityTelecommunications

Abstract

fetched live from OpenAlex

IEEE 802.11ah protocol is specifically designed to provide network connectivity to a large number of energy efficient heterogeneous internet of things (IoT) devices. Restricted access window (RAW) mechanism of the protocol is an innovative feature which aims at reducing medium access contention by slotting the beacon interval and allowing limited number of nodes to contend in a specific slot. In this paper, we evaluate important medium access control (MAC) layer performance metrics of differentiated quality of service (QoS) heterogeneous IoT nodes in IEEE 802.11ah RAW mechanism. Our analysis evaluates the feasibility of coexistence of priority and non-priority traffic in IoT devices without degrading network performance.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score0.585

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 head0.019
GPT teacher head0.268
Teacher spread0.249 · 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 designBench or experimental
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

Citations2
Published2018
Admission routes1
Has abstractyes

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