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Record W2086143121 · doi:10.1109/milcom.2007.4454951

Multicast Forwarding Using Multiple Gateways and Hash for Duplicate Packet Detection in a Tactical MANET

2007· article· en· W2086143121 on OpenAlexaff
Lars Landmark, Yannick Lacharite, Louise Lamont

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsComputer networkMulticastComputer scienceProtocol Independent MulticastSource-specific multicastXcastPragmatic General MulticastDistance Vector Multicast Routing ProtocolIP multicastDistributed computingNetwork packetReliable multicast

Abstract

fetched live from OpenAlex

Simplified Multicast Forwarding (SMF) [3] provides an optimized flooding mechanism in MANET environments to efficiently propagate multicast packets. In order to be more useful in a broader range of scenarios, we need to be able to get multicast packets to and from fixed infrastructures. Gateways in MANETs are more complex than regular MANET nodes as they require both to join multicast groups on behalf of the MANET, as well as to forward multicast packets between networks. Multicast gateways are required to interoperate with other multicast routing protocols in the wired domain. In this paper we show that by using a hash function we are able to assign packets with a common unique packet identifier that is further used for duplicate packet detection. Using the hash function in conjunction with multiple gateways, we lower the traffic overhead, increase the packet delivery ratio, and make the protocol more resilient to network partitioning as well as independent to the number of gateways.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.975
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.284
Teacher spread0.253 · 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 teacher head, 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

Citations3
Published2007
Admission routes1
Has abstractyes

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