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Record W2115026050 · doi:10.1504/ijwmc.2013.053040

On routing protocols using mobile social networks

2013· article· en· W2115026050 on OpenAlexaff
Ahmed B. Altamimi, T. Aaron Gulliver

Bibliographic record

VenueInternational Journal of Wireless and Mobile Computing · 2013
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceComputer networkRouting protocolWireless Routing ProtocolNode (physics)Link-state routing protocolDynamic Source RoutingDestination-Sequenced Distance Vector routingSoftware deploymentDistributed computingPolicy-based routingMobile social networkRouting (electronic design automation)Mobile computing

Abstract

fetched live from OpenAlex

A Mobile Social Network (MSN) is defined as a mobile network that uses social relationships to determine node communication. Many wireless networks including ad hoc networks do not reflect a real world deployment because of routing implementation difficulties. However, with the enormous use of Social Network Sites (SNSs) including Twitter and Facebook, MSNs can be exploited to make routing easier. Although there has been some research effort devoted to routing using these networks, the MSN routing protocols proposed in the literature suffer from either a low delivery ratio or high memory requirements. This paper presents a new routing protocol (status) for MSNs which has excellent performance in terms of delivery ratio and memory requirements. It employs the online status of a node to make forwarding decisions. Status has a low overhead ratio, low average delay and low computational complexity at the node level.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0020.003
Scholarly communication0.0030.009
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.022
GPT teacher head0.304
Teacher spread0.282 · 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

Citations12
Published2013
Admission routes1
Has abstractyes

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