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Record W2040855320 · doi:10.1109/iwcmc.2013.6583748

A recursive solution for improving the synchronization accuracy in wireless sensor networks

2013· article· en· W2040855320 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
FieldComputer Science
TopicNetwork Time Synchronization Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceSynchronization (alternating current)Flexibility (engineering)Convergence (economics)Distributed computingStability (learning theory)Clock synchronizationWireless sensor networkSimple (philosophy)Protocol (science)Core (optical fiber)Mathematical optimizationComputer networkChannel (broadcasting)Mathematics

Abstract

fetched live from OpenAlex

A two-way message exchange mechanism is at the core of many clock synchronization protocols. The basic concept of this method has been designed for symmetrical links, otherwise, depending on the level of asymmetric delays, large synchronization errors may occur. Many estimation based methods have been proposed so far for estimating the degree of asymmetry and reducing this error. However, many of them are solved for a specific distribution of link random delays. Moreover, imposing a high level of computational complexity upon network nodes is another problem of them. In this paper we introduce a recursive solution for improving the performance of the basic two-way message exchange mechanism. The advantage of this method is that it keeps the protocol simple and therefore easily implementable. The proposed method does not make any specific assumption about the distribution of the random delays. It also provides the network designer with some deployment flexibility, trading off convergence speed against stability.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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