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Record W2033855027 · doi:10.1109/mpot.2013.2286692

Body Area Sensor Networks: Requirements, Operations, and Challenges

2014· article· en· W2033855027 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

VenueIEEE Potentials · 2014
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEavesdroppingWireless sensor networkComputer scienceWirelessBody area networkRisk analysis (engineering)Energy consumptionPopulationSystems engineeringComputer securityEngineeringTelecommunicationsComputer networkElectrical engineering

Abstract

fetched live from OpenAlex

This article provides an overview of body area sensor networks (BASNs), an application of wireless technology that changed health care to suit the comfort of the population. There are various applications of BASNs and attempts have been made to make the human body a channel for wireless communication. BASNs employ a three-tiered architectural system that requires various technical requirements for its optimal and efficient operation. Energy consumption is one of the major issues that is currently being addressed through self-harvesting and many other techniques. Even with the benefits at hand, there are various issues such as interference and eavesdropping that BASNs have to tackle. Biometrics is a widely used solution. Researchers are also working on various ambitious projects that deal with improving deep brain simulation, heart regulation, drug delivery, and prosthetic actuation to use BASN effectively.

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: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.837

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 head0.026
GPT teacher head0.224
Teacher spread0.198 · 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