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Record W2156528695 · doi:10.1109/mnet.2010.5464224

Biologically inspired consensus-based spectrum sensing in mobile Ad Hoc networks with cognitive radios

2010· article· en· W2156528695 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 · 2010
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsDefence Research and Development CanadaCarleton University
Fundersnot available
KeywordsCognitive radioComputer scienceMobile ad hoc networkWireless ad hoc networkScheme (mathematics)Computer networkNode (physics)Spectrum (functional analysis)Distributed computingSet (abstract data type)TelecommunicationsWireless

Abstract

fetched live from OpenAlex

Cognitive radios, which are capable of sensing their surrounding environment and adapting their internal parameters, have been considered in mobile ad hoc networks. Secondary users can cooperatively sense the spectrum to detect the presence of primary users. In this article we present a novel biologically inspired consensus-based cooperative spectrum sensing scheme in CR-MANETs. Our scheme is based on recent advances in consensus algorithms that have taken inspiration from self-organizing behavior of animal groups such as birds, fish, ants, honeybees, and others. Unlike the existing cooperative spectrum sensing schemes, such as the OR-rule or the 1-out-of-N rule, there is no need for a common receiver to do the data fusion for reaching the final decision. A secondary user needs only to set up local interactions without a centralized node in CR-MANETs. Simulation results are presented to show the effectiveness of the proposed scheme.

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 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: Empirical
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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