MétaCan
Menu
Back to cohort
Record W2104867783 · doi:10.1109/enc.2009.15

Partial Delaunay Triangulations Based Data-Centric Storage and Routing with Guaranteed Delivery in Wireless Ad Hoc and Sensor Networks

2009· article· en· W2104867783 on OpenAlexafffund
Yanli Deng, Ivan Stojmenović

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDelaunay triangulationComputer scienceUnit disk graphWireless ad hoc networkRouting (electronic design automation)Destination-Sequenced Distance Vector routingComputer networkGeographic routingDistributed computingDynamic Source RoutingWirelessAlgorithmWireless networkRouting protocol

Abstract

fetched live from OpenAlex

Existing memory-less greedy-face-greedy (GFG) routing algorithm can guarantee the delivery in wireless ad hoc networks modeled by a connected unit disk graph. The FACE mode in GFG routing is a recovery mode used when no neighbor closer to destination exists. Face mode requires extracting a planar sub graph out of the unit disk graph. In this paper, we propose to apply partial Delaunay triangulation (PDT) instead of Gabriel graph (GG) used in the original GFG routing. PDT is locally defined without any message exchange in addition to those needed to learn locations of neighbors. This appears to be the densest known such message free planar graph. PDT can be used instead of GG for storing data in wireless sensor networks, where each datum is stored in the face of PDT containing hashed location of datum. Simulation results show the GFG routing has better performance on a PDT than on a GG, since PDT is denser than GG. Applying dominating set based routing or a shortcut scheme can further enhance the PDT-based routing performance.

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.

How this classification was reachedexpand

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 score0.553
Threshold uncertainty score0.364

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 science0.0000.000
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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2009
Admission routes2
Has abstractyes

Explore more

Same topicComputational Geometry and Mesh GenerationFrench-language works237,207