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Record W2059353064 · doi:10.1109/vetecf.2010.5594488

DTcoop: Delay Tolerant Cooperative Communications in DTN/WLAN Integrated Networks

2010· article· en· W2059353064 on OpenAlexaff
Hao Liang, Weihua Zhuang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer networkComputer scienceUploadNode (physics)Local area networkDelay-tolerant networkingWirelessThe InternetScheduling (production processes)DisseminationTransmission (telecommunications)Wi-FiWireless networkRouting protocolNetwork packetTelecommunicationsEngineeringOptimized Link State Routing ProtocolOperating system

Abstract

fetched live from OpenAlex

In this paper, we consider a DTN/WLAN integrated network where nomadic nodes with high mobility comprise a delay tolerant network (DTN) while local nodes with low mobility reside in the coverage area of wireless local area networks (WLANs). A message dissemination service is considered, where data traffic is generated by a server in the Internet and destined to a group of nomadic nodes. In order to facilitate message dissemination, a delay tolerant cooperative communication (DTCoop) scheme is proposed. The messages for dissemination are first pre-downloaded to a group of storage local nodes within a WLAN before the visit of a nomadic node, and then scheduled for transmission when a nomadic node comes into the transmission range. Analysis and simulation results are presented to evaluate the performance of the proposed DTCoop scheme. It is shown that our proposed scheme can significantly improve the message delivery performance from a WLAN to a nomadic node as compared with existing schemes without message pre-downloading or message scheduling.

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 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: Methods · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.025
GPT teacher head0.270
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations7
Published2010
Admission routes1
Has abstractyes

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