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Record W2058388278 · doi:10.1109/comst.2014.2386799

Cooperative Routing in Wireless Networks: A Comprehensive Survey

2014· article· en· W2058388278 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 Communications Surveys & Tutorials · 2014
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer scienceMultipath routingComputer networkPolicy-based routingLink-state routing protocolDynamic Source RoutingStatic routingRouting protocolDistributed computingCooperative diversityRouting (electronic design automation)FadingChannel (broadcasting)

Abstract

fetched live from OpenAlex

Cooperative diversity has gained much interest due to its ability to mitigate multipath fading without using multiple antennas. There has been considerable research on how cooperative transmission can improve the performance of the physical layer. During the past few years, the researchers have started to take into consideration cooperative transmission in routing, and there has been a growing interest in designing and evaluating cooperative routing protocols. Routing algorithms that take into consideration the availability of cooperative transmission at the physical layer are known as cooperative routing algorithms. This paper presents a comprehensive survey of the existing cooperative routing techniques, together with the highlights of the performance of each strategy. This survey also provides a taxonomy of different cooperative routing protocols and outlines the fundamental components and challenges associated with cooperative routing objectives. Existing cooperative routing algorithms are compared and lay the groundwork for further research.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
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 score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0050.002
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 head0.095
GPT teacher head0.329
Teacher spread0.234 · 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