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Record W2049792773 · doi:10.1109/iccs.2010.5685906

A Markov model for IEEE 802.15.4 MAC with GTS transmissions and heterogeneous traffic in non-saturation mode

2010· article· en· W2049792773 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceComputer networkIEEE 802.15Markov chainNetwork packetMarkov processBandwidth (computing)WirelessMarkov modelIEEE 802.11Real-time computingQuality of serviceWireless networkWireless sensor networkTelecommunications

Abstract

fetched live from OpenAlex

In wireless personal area networks such as wireless body area sensor networks, stations or devices have different bandwidth requirements and thus create heterogeneous traffic. For such networks, the IEEE 802.15.4 MAC can be used in the beacon-enabled mode which supports guaranteed time slot (GTS) allocation for time-critical data transmissions. This paper presents a general discrete-time Markov chain model for the IEEE 802.15.4-based networks taking into account the slotted CSMA/CA and GTS transmission phenomena together in heterogeneous traffic scenario and non-saturated condition. For this purpose, the standard GTS allocation scheme is modified. For each non-identical device, the Markov model is solved and the average service time and the service utilization factor are analyzed in the non-saturation mode. The utilization factor is higher for the higher data rate devices. The GTS transmission scheme also results in higher utilization for the devices. The analysis is validated by simulations using Network Simulator (NS) version 2.33.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.653
Threshold uncertainty score0.395

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 science0.0000.000
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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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

Quick stats

Citations13
Published2010
Admission routes1
Has abstractyes

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