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Record W2093889114 · doi:10.4018/jbdcn.2007010101

Cooperative Data Caching and Prefetching in Wireless Ad Hoc Networks

2007· article· en· W2093889114 on OpenAlexaff
Mieso K. Denko

Bibliographic record

VenueInternational Journal of Business Data Communications and Networking · 2007
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsInstruction prefetchComputer scienceCacheComputer networkMobile ad hoc networkOverhead (engineering)Wireless ad hoc networkConsistency (knowledge bases)Scheme (mathematics)CPU cacheCluster analysisDistributed computingWirelessOperating system

Abstract

fetched live from OpenAlex

This article proposes a cooperative data caching and prefetching scheme for Mobile Ad Hoc Networks (MANETs). In this scheme, multiple hosts cooperate in both prefetching and caching commonly used data. To reduce communication and computational overhead, we use a clustering architecture for the network organization. A weak consistency based on time to live value was used to maintain data consistency. A hybrid cache replacement policy that uses frequency of access and the reference time was employed. The effects of cache size, mobility, and prefetching threshold on the network performance were investigated in a discrete event simulation environment. The contribution of intra-cluster and inter-cluster information to overall data accessibility ratio was also investigated. The simulation results indicate that the proposed scheme improves both data accessibility and query delay at relatively lower prefetch thresholds, larger cache sizes, and moderate mobility.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.000
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 head0.075
GPT teacher head0.333
Teacher spread0.258 · 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

Citations21
Published2007
Admission routes1
Has abstractyes

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