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Record W2167762027 · doi:10.1109/ipdps.2000.846008

Power-aware localized routing in wireless networks

2002· article· en· W2167762027 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceRouting (electronic design automation)Computer networkMetricsMetric (unit)Equal-cost multi-path routingPower (physics)Geographic routingStatic routingMathematical optimizationRouting protocolMathematicsEngineering

Abstract

fetched live from OpenAlex

Two metrics where transmission power depends on distance between nodes, and a cost aware metric based on remaining battery power at nodes (assuming constant transmission power), together with corresponding non-localized shortest path routing algorithms, were recently proposed. We define a new power-cost metric based on the combination of both node's lifetime and distance based power metrics. We then propose power, cost, and power-cost GPS based localized routing algorithms, where nodes make routing decisions solely on the basis of location of their neighbors and destination. Power aware localized routing algorithm attempts to minimize the total power needed to route a message between a source and a destination. Cost-aware localized algorithm is aimed at extending battery's worst case lifetime. The combined power-cost algorithm attempts to minimize the total power needed and to avoid nodes with short remaining lifetime. We prove that these localized power, cost, and power-cost efficient routing algorithms are loop-free.

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)0.000
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 score0.955
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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

Quick stats

Citations135
Published2002
Admission routes1
Has abstractyes

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