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Record W1516842272 · doi:10.1109/icc.2015.7249353

Guarding an area of interest in sensor grids with unreliable nodes

2015· article· en· W1516842272 on OpenAlexafffund
Mohammed Elmorsy, Ehab S. Elmallah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsDependabilityComputer scienceWireless sensor networkGuard (computer science)Network topologyReliability (semiconductor)GridDistributed computingIntrusion detection systemMeasure (data warehouse)Grid networkComputer networkData miningMathematics

Abstract

fetched live from OpenAlex

We consider Wireless Sensor Networks (WSNs) deployed in the plane to guard against intrusion events aiming to access a specified area of interest. Sensor nodes of the network are assumed to be unreliable with known failure probabilities. In such an environment, system dependability is of prime importance. To aid in analyzing dependability, we formalize a network wide reliability measure that quantifies the likelihood that the network provides simultaneous detection and reporting of intrusion events. We refer to the problem of computing the defined measure as the breach path to target area reliability (BPTA-REL) problem. We show that the problem admits polynomial time solution on grid networks employing diagonal links where the width of a grid is limited but the length can be arbitrarily large. Such grid topologies are useful for border area protection applications. The result is notable since the BPTA-REL problem is #P-hard in general. We present numerical results that show the potential use of our devised algorithm as a network design tool.

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: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.211

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 science0.0000.000
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 head0.076
GPT teacher head0.237
Teacher spread0.161 · 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

Citations2
Published2015
Admission routes2
Has abstractyes

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