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Record W2081531839 · doi:10.1109/tpds.2011.179

DCS: Distributed Asynchronous Clock Synchronization in Delay Tolerant Networks

2011· article· en· W2081531839 on OpenAlexaff
Bong Jun Choi, Hao Liang, Xuemin Shen, Weihua Zhuang

Bibliographic record

VenueIEEE Transactions on Parallel and Distributed Systems · 2011
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsClock synchronizationAsynchronous communicationComputer scienceSynchronization (alternating current)Clock driftConvergence (economics)Self-clocking signalMaster clockProtocol (science)Clock networkNetwork Time ProtocolComputer networkDistributed computingReal-time computingClock skewClock signalTime synchronizationJitter

Abstract

fetched live from OpenAlex

In this paper, we propose a distributed asynchronous clock synchronization (DCS) protocol for Delay Tolerant Networks (DTNs). Different from existing clock synchronization protocols, the proposed DCS protocol can achieve global clock synchronization among mobile nodes within the network over asynchronous and intermittent connections with long delays. Convergence of the clock values can be reached by compensating for clock errors using mutual relative clock information that is propagated in the network by contacted nodes. The level of clock accuracy is depreciated with respect to time in order to account for long delays between contact opportunities. Mathematical analysis and simulation results for various network scenarios are presented to demonstrate the convergence and performance of the DCS protocol. It is shown that the DCS protocol can achieve faster clock convergence speed and, as a result, reduces energy cost by half for neighbor discovery.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.217
Teacher spread0.193 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations92
Published2011
Admission routes1
Has abstractyes

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