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Record W2076540175 · doi:10.1109/icc.2010.5502692

An Optimization-Based GTS Allocation Scheme for IEEE 802.15.4 MAC with Application to Wireless Body-Area Sensor Networks

2010· article· en· W2076540175 on OpenAlexaff
B. Shrestha, Ekram Hossain, Sergio Camorlinga, Ramesh Krishnamoorthy, Dusit Niyato

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIEEE 802.15Computer scienceComputer networkWireless sensor networkBandwidth allocationNetwork packetKnapsack problemScheme (mathematics)IEEE 802Network allocation vectorReliability (semiconductor)WirelessInter-Access Point ProtocolBandwidth (computing)Wireless networkIEEE 802.11Quality of serviceWi-Fi arrayAlgorithmTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

IEEE 802.15.4 standard is widely used in wireless personal area networks (WPANs). This standard supports a limited number of guaranteed time slots (GTSs) for time-critical or delay-sensitive data transmission.We propose a GTS allocation scheme to improve reliability and bandwidth utilization in IEEE 802.15.4-based wireless body area sensor networks (WiBaSe-Nets). A knapsack problem is formulated to obtain optimal GTS allocation such that a minimum bandwidth requirement is satisfied for the sensor devices. Simulation results show that the proposed scheme can achieve better GTS utilization and higher packet delivery ratio than the standard IEEE 802.15.4 scheme does.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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

Citations31
Published2010
Admission routes1
Has abstractyes

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