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Record W2096950527 · doi:10.1109/milcom.2008.4753081

Scalable Team Oriented Reliable Multicast routing protocol for tactical mobile ad hoc networks

2008· article· en· W2096950527 on OpenAlexaff
Emy E. Egbogah, Abraham O. Fapojuwo, Norbert Chan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsGeneral Dynamics (Canada)University of Calgary
Fundersnot available
KeywordsComputer networkProtocol Independent MulticastComputer scienceMulticastXcastPragmatic General MulticastSource-specific multicastDistance Vector Multicast Routing ProtocolDistributed computingReliable multicastIP multicastMobile ad hoc networkGeocastRouting protocolWireless Routing ProtocolRouting (electronic design automation)

Abstract

fetched live from OpenAlex

This paper presents the scalable team oriented reliable multicast (STORM) routing protocol, designed to provide reliability in a tactical mobile ad hoc network (MANET) under a wide range of scenarios and network conditions. STORM organizes individual nodes with similar mobility patterns and speeds into teams, and builds a hierarchy-based multicast mesh structure among elected team nodes. A unicast acknowledgement scheme (UAS) is developed to build the routing structure in an efficient manner. To improve the reliability of STORM, a modified version of reliable adaptive congestion controlled multicast (ReACT) is used as a reliable transport protocol. The simulated performance results indicate that STORM is scalable with respect to number of multicast groups, multicast sources, and network load. Furthermore, the adoption of ReACT as a transport protocol improves the performance of STORM in conditions of heavy network traffic.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.289
Teacher spread0.266 · 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

Citations11
Published2008
Admission routes1
Has abstractyes

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