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Record W2126775139 · doi:10.1109/icdcs.2002.1022287

On-demand multicasting in ad-hoc networks:comparing AODV and ODMRP

2003· article· en· W2126775139 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsCarleton University
Fundersnot available
KeywordsMulticastComputer networkComputer scienceProtocol Independent MulticastAd hoc On-Demand Distance Vector RoutingGeocastDistributed computingDistance Vector Multicast Routing ProtocolMobile ad hoc networkWireless ad hoc networkXcastOptimized Link State Routing ProtocolRouting protocolRouting (electronic design automation)WirelessTelecommunications

Abstract

fetched live from OpenAlex

Multicasting can efficiently support a wide variety of applications that are characterized by a close degree of collaboration, typical for many mobile ad-hoc network (MANET) applications currently envisioned. To deal with the specific characteristics of MANETs, new multicast protocols that operate in an on-demand manner are being proposed and investigated. Our results show that a tree-based on-demand protocol is not necessarily the best choice. A mesh-based protocol can outperform tree-based protocols, due to the availability of alternative paths, which allow multicast datagrams to be delivered to all or most multicast receivers even if links fail.

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.

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 score0.786
Threshold uncertainty score0.599

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 head0.017
GPT teacher head0.238
Teacher spread0.220 · 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

Quick stats

Citations45
Published2003
Admission routes1
Has abstractyes

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