MétaCan
Menu
Back to cohort
Record W2155615280 · doi:10.1109/icc.2009.5199248

On Sensor Placement for Directional Wireless Sensor Networks

2009· article· en· W2155615280 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 institutionsCarleton University
Fundersnot available
KeywordsWireless sensor networkComputer scienceSet (abstract data type)Task (project management)Key distribution in wireless sensor networksReal-time computingInteger programmingLinear programmingWirelessComputer networkEngineeringTelecommunicationsWireless networkAlgorithm

Abstract

fetched live from OpenAlex

A directional sensor network is formed by directional sensors which may be oriented toward different directions. The sensing region of a directional sensor can be viewed as a sector in a two-dimensional plane. Therefore, a directional sensor can only choose one sector (or direction) at any time instant. Planning of directional sensor networks has received very little attention in the literature. In this paper, we discuss directional sensor placement which is a critical task in the planning of directional sensor networks. We also present an integer linear programming model whose goal is to minimize the number of directional sensors that need to be deployed to monitor a set of discrete targets in a sensor field. Numerical results demonstrate the viability and effectiveness of the model.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.236
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

Quick stats

Citations30
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicEnergy Efficient Wireless Sensor NetworksFrench-language works237,207