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Record W1529752038 · doi:10.1109/wimob.2005.1512918

On the potential of hybrid route/relay schemes

2006· article· en· W1529752038 on OpenAlexaff
Tianhao Qiu, Ioanis Nikolaidis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceComputer networkRedundancy (engineering)RelayTree traversalNetwork packetThroughputMobile ad hoc networkWireless ad hoc networkMobility modelRouting (electronic design automation)Transmission delayDistributed computingWirelessTelecommunicationsAlgorithm

Abstract

fetched live from OpenAlex

The objective of increasing capacity in mobile ad-hoc networks (MANETs) by exploiting the mobility of nodes has produced interesting but rather unrealistic results. The throughput improvement is not followed by reasonable delay performance, and in realistic settings there is a need to compensate for the potentially low mobility of all, or parts of, the network. We explore the throughput/delay tradeoff by attempting to utilize all possible service (transmission) opportunities that may exist. To this end, a measure of redundancy is introduced and its impact characterized. The definition of redundancy admits both the replication of data items as well as the traversal of paths. We study three schemes, where the third is a new proposal for a hybrid scheme that balances off a limited form of routing with relayed packet propagation. We find that the hybrid is resilient to the impact of mobility, providing good performance even at low mobility settings.

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)0.008
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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.004
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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