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Record W2063324885 · doi:10.1109/glocom.2012.6503123

Energy efficient scheduling for delay constrained communication in wireless body area networks

2012· article· en· W2063324885 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
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceLyapunov optimizationEnergy consumptionScheduling (production processes)Transmission delayWireless sensor networkData transmissionEfficient energy useWirelessDistributed computingPropagation delayComputer networkReal-time computingMathematical optimizationNetwork packetTelecommunicationsChaoticEngineering

Abstract

fetched live from OpenAlex

Delay constraint and sensor energy consumption requirements are two core issues for e-healthcare applications in wireless body area networks. In this paper, we investigate the data transmission scheduling problem to utilize the sleep and opportunistic transmission for energy efficiency, while guaran- teeing the worst-case delay for medical data transmission. To achieve sensor energy saving by exploiting propagation channel quality with a deterministic delay requirement poses challenges in developing a scheduling policy. We address this problem using a Lyapunov optimization formulation and propose a two-step scheduling algorithm. We prove that the algorithm can provide worst-case delay guarantee under certain conditions. Theoretical analysis and simulation results are presented to demonstrate the tradeoff between the transmission delay and energy 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 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 score0.898
Threshold uncertainty score0.624

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations19
Published2012
Admission routes1
Has abstractyes

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