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Record W2169947777

Dynamic coalition formation for efficient sleep time allocation in wireless sensor networks using cooperative game theory

2009· article· en· W2169947777 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

VenueInternational Conference on Information Fusion · 2009
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWireless sensor networkCore (optical fiber)Computer scienceGame theoryMathematical optimizationUpper and lower boundsCramér–Rao boundWirelessTime allocationRegular polygonConvex optimizationSleep (system call)Cooperative game theoryAlgorithmComputer networkMathematicsTelecommunicationsEstimation theoryMathematical economics
DOInot available

Abstract

fetched live from OpenAlex

This paper proposes a dynamic coalition formation algorithm for efficient sleep time allocation in a wireless sensor network (WSN) deployed to localize targets based on cooperation among the nodes. The sleep time allocation problem is formulated as a non-convex cooperative game and the concept of the core is exploited to solve this problem. In this formulation, determinant of the Cramer-Rao lower bound (CRLB) is used to evaluate the accuracy of estimations. Finally, an algorithm is proposed based on a best-reply rule which converges to the core with probability one if the core of the game is non-empty.

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 score0.946
Threshold uncertainty score0.885

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 communication0.0000.002
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 head0.018
GPT teacher head0.271
Teacher spread0.254 · 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