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Record W2005686751 · doi:10.1109/lwc.2014.2357032

Joint Cooperative Routing and Power Allocation for Collision Minimization in Wireless Sensor Networks With Multiple Flows

2014· article· en· W2005686751 on OpenAlex
Fatemeh Mansourkiaie, Mohammed Hossam Ahmed

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 Wireless Communications Letters · 2014
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer scienceCollisionRouting (electronic design automation)Computer networkMathematical optimizationDynamic Source RoutingStatic routingLink-state routing protocolMinificationTime constraintConstraint (computer-aided design)Equal-cost multi-path routingRouting protocolDistributed computingEngineeringMathematics

Abstract

fetched live from OpenAlex

In this letter, a cross-layer cooperative routing algorithm is proposed for minimizing the collision probability subject to an end-to-end outage probability constraint. We develop a collision minimization algorithm by combining cooperative transmission, optimal power allocation, and route selection. The proposed cooperative routing algorithm, called minimum collision cooperative routing (MCCR), selects the route that causes minimum collision probability to other nodes in the network. Results show that MCCR can significantly reduce the collision probability compared with existing cooperative routing schemes.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.658
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.028
GPT teacher head0.253
Teacher spread0.225 · 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