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Record W2323960072 · doi:10.3724/sp.j.1001.2013.04227

Periodic Intermittently Connected-Based Data Delivery in Opportunistic Networks

2014· article· en· W2323960072 on OpenAlexaff
Lei Wu, Dean Wu, Ming Liu, Xiaomin Wang, Haigang Gong

Bibliographic record

VenueJournal of Software · 2014
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsPoultry Industry Council
Fundersnot available
KeywordsComputer scienceNode (physics)Probability distributionComputer networkTopology (electrical circuits)MathematicsPhysicsStatistics

Abstract

fetched live from OpenAlex

提出一种在机会网络中基于周期性间歇连通的数据传输策略PICD(periodic intermittently connectedbaseddata delivery in opportunistic networks).通过有效利用节点间的周期间歇连通性改善数据传输性能.节点传输概率的计算则充分考虑了其与汇聚点间存在的间歇多跳路径,并将其与消息容忍的传输延迟相结合.首先,采用随机动态规划的方法建立与延迟相关的传输概率模型;然后,通过基于多跳的函数空间迭代法求出一个周期内的与延迟相关的传输概率分布矩阵;节点面向不同消息延迟的传输概率则基于分布矩阵计算获得,以此作为选择下一跳的依据.与延迟相关的概率转发机制提高了消息在容忍的延迟内被成功递交的可能.仿真实验结果表明,与现有的几种数据传输算法相比,在节点具有循环运动特征的环境下,PICD具有较高的数据传输成功率和较低的递交延迟.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.247
Teacher spread0.213 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations5
Published2014
Admission routes1
Has abstractyes

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