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Record W2784046327 · doi:10.5555/3242181.3242539

Network layer connectivity awareness with application to investigate the OLSR protocol in tactical manets

2017· article· en· W2784046327 on OpenAlexaff
Ming Li, Mazda Salmanian, T.J. Willink

Bibliographic record

VenueWinter Simulation Conference · 2017
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsComputer networkComputer scienceOptimized Link State Routing ProtocolRouting protocolNetwork packetMobile ad hoc networkWireless ad hoc networkLink-state routing protocolMetricsNetwork layerWireless Routing ProtocolDistributed computingLink layerMetric (unit)Ad hoc wireless distribution servicePhysical layerZone Routing ProtocolLayer (electronics)WirelessEngineeringTelecommunications

Abstract

fetched live from OpenAlex

We propose a new local networking metric, the network layer connectivity awareness (NLCA), to dynamically characterize the connectivity status at the network layer of mobile ad hoc networks (MANETs). The NLCA is a local view of routable destinations provided by a designated routing protocol, which may differ from the real-time physical layer connectivity (PHYCON), defined as destinations that can be reached by local nodes via (multi-hop) radio links. Such discrepancy can cause packet delivery failure because a route may no longer be available at physical layer. We present a simulation method to obtain the real-time PHYCON using the breadth-first search algorithm. We apply the NCLA metric to the optimized link state routing (OLSR) protocol in scenarios simulating tactical MANETs, and compare the resulting NLCA with the underlying PHYCON, illustrating the two measurements vary and differ under mobility. The proposed NLCA and investigation technique provide a method for routing performance analysis.

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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.338
Teacher spread0.284 · 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
Published2017
Admission routes1
Has abstractyes

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