MétaCan
Menu
Back to cohort
Record W2020988785 · doi:10.1109/pimrc.2011.6139904

Action-based scheduling technique for 802.15.4/ZigBee wireless body area networks

2011· article· en· W2020988785 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
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceComputer networkScheduling (production processes)Wireless sensor networkWirelessEnergy consumptionPower consumptionSocial connectednessDistributed computingWireless networkPower (physics)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Energy-efficient communication protocols in resource-constrained networks and specifically wireless body area networks (WBANs) has been of significant importance since they emerged nearly last decade. In this work, we use the periodic nature of body actions to propose an action-based scheduling technique in which time-slot allocations are adapted to the periodic connectedness of on-body links. In other words, the periodicity of on-body links is employed to predict the future behaviours of links to help develop energy-efficient communications between on-body nodes, thereby elongating the network lifetime. Analysis and measurement with 2.4GHz IEEE 802.15.4/ZigBee compliant micaZ motes in a fitness environment serve as our tool to do action recognition and subsequently scheduling. The proposed technique helps us reach within less than 7% of power consumption lower bound while it does not have complexity of most channel prediction algorithms that can result in excessive process power consumption.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score1.000

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 head0.030
GPT teacher head0.232
Teacher spread0.202 · 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

Citations14
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicWireless Body Area NetworksFrench-language works237,207