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Record W2893834508 · doi:10.1109/mnet.2018.1700297

Joint Opportunistic Routing and Intra-Flow Network Coding in Multi-Hop Wireless Networks: A Survey

2018· article· en· W2893834508 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

VenueIEEE Network · 2018
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer scienceComputer networkWireless networkRouting protocolLinear network codingWirelessWireless Routing ProtocolDynamic Source RoutingDistributed computingLossy compressionMultiple description codingRouting (electronic design automation)TelecommunicationsNetwork packetArtificial intelligence

Abstract

fetched live from OpenAlex

Opportunistic routing and network coding are two promising techniques that have been proposed for wireless networks. These techniques have significantly improved the performance of multi-hop wireless networks by utilizing the broadcast nature of wireless media and optimizing the capacity of lossy wireless networks. Recent research has shown that the combination of opportunistic routing and network coding in a single joint protocol outperforms each of them individually. This article explains the motivation and interaction effect of the joint protocols. We provide a taxonomy of joint protocols and illustrate the benefit and cost by highlighting their fundamental components and comparing different solutions. We also present a conclusion along with the outline of future research direction.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.102
GPT teacher head0.300
Teacher spread0.197 · 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