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Record W2077045913 · doi:10.1109/wmnc.2014.6878853

Routing in unmanned aerial ad hoc networks: Introducing a route reliability criterion

2014· article· en· W2077045913 on OpenAlexaff
Jean-Daniel Medjo Me Biomo, Thomas Kunz, Marc St‐Hilaire

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceReliability (semiconductor)Computer networkWireless ad hoc networkAd hoc On-Demand Distance Vector RoutingRouting (electronic design automation)Routing protocolSelection (genetic algorithm)Optimized Link State Routing ProtocolDistributed computingTelecommunicationsWirelessArtificial intelligence

Abstract

fetched live from OpenAlex

The Reactive-Greedy-Reactive (RGR) protocol, as proposed in [2], is a routing protocol specifically designed for unmanned aeronautical ad hoc networks. Since RGR is based on the Ad Hoc On-Demand Distance Vector (AODV), the routes with the least number of hops are ultimately preferred during the route discovery. Overall, freshness and path length (in hops) are the two criteria that govern route selection. In this paper, we improve the process of route selection in RGR by adding a criterion based on route reliability/stability. Route stability here is measured by means of a concept called reliable distance. Route selection will prefer reliable routes before considering route length and freshness. Simulations in Opnet show a considerable improvement in performance at virtually no additional cost.

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)0.003
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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
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

Citations39
Published2014
Admission routes1
Has abstractyes

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