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Record W2140353984 · doi:10.1109/wocn.2005.1436038

Replica update strategies in mobile ad hoc networks

2005· article· en· W2140353984 on OpenAlexaff
Hua Lu, Mieso K. Denko

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsComputer scienceReplicaMobile ad hoc networkComputer networkUnavailabilityWireless ad hoc networkDistributed computingScalabilityOverhead (engineering)Node (physics)Consistency (knowledge bases)Vehicular ad hoc networkNetwork topologyMobile computingData consistencyConsistency modelEventual consistencyEngineeringDatabaseWirelessTelecommunications

Abstract

fetched live from OpenAlex

Since every node can be mobile and the network topology and resources can change frequently, disconnected operation is a common mode in mobile ad hoc networks (MANETs). Disconnected operation and weak connectivity can cause data unavailability. Caching and replica allocation within network can improve data accessibility by storing the data and accessing it locally. However, maintaining data consistency among replicas becomes a challenging problem. Hence, balancing data accessibility and consistency is an important step towards data management in ad hoc networks. In this paper, we propose replica update strategies to maintain data consistency while improving data accessibility in ad hoc networks. Our solution is based on a clustered network where nodes in the network are divided into small and manageable groups to reduce communication overhead, support localized operations and enhance scalability. A simulation environment was built using an ns2 network simulator to evaluate the performance of the proposed schemes. The results show that our schemes distribute replicas effectively and provide high data accessibility rate.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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 designOther design
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

Citations4
Published2005
Admission routes1
Has abstractyes

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