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Record W1998094594 · doi:10.1109/wcnc.2012.6214267

A distributed and adaptive routing protocol designed for wireless sensor networks deployed in clinical environments

2012· article· en· W1998094594 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 institutionsMcGill University
Fundersnot available
KeywordsComputer networkComputer scienceRouting protocolEMIWireless Routing ProtocolWireless sensor networkScalabilityElectromagnetic interferenceZone Routing ProtocolLink-state routing protocolDynamic Source RoutingRouting (electronic design automation)Distributed computingTelecommunications

Abstract

fetched live from OpenAlex

The effects of electromagnetic interference (EMI) on operations of sensitive medical devices have been recognized as a critical concern related to safety in hospitals and healthcare institutions. This paper proposes an adaptive and distributed routing protocol that attempts to reduce the EMI introduced by a medical wireless sensor network (MWSN). The proposed algorithm, namely EMI-aware routing protocol (EMIR), assigns to each node a potential value which is dynamically calculated in such a way that network traffic tends to be deflected from nodes that are radiating high EMI and/or locating far away from gateways. Experiments in a real-life IEEE 802.15.4-based WSN implemented with the EMIR demonstrate that, compared to the shortest path routing, the proposed algorithm can significantly suppress the level of the EMI in the surrounding area where the WSN is deployed. Besides, the EMIR is scalable to the network size because it only requires one-hop neighbor information.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.275
Teacher spread0.248 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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