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Record W2898820452 · doi:10.1177/1550147718810692

Reliability evaluation of wireless multimedia sensor networks based on instantaneous availability

2018· article· en· W2898820452 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 Journal of Distributed Sensor Networks · 2018
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsComputer scienceReliability (semiconductor)Wireless sensor networkWireless networkWirelessProcess (computing)Markov processPercolation (cognitive psychology)Laplace transformMarkov chainReliability engineeringComputer networkMachine learningTelecommunications

Abstract

fetched live from OpenAlex

With the widespread application of wireless multimedia sensor networks, the issue of network reliability has attracted more and more attention. In this article, a new reliability evaluation method of wireless multimedia sensor network is proposed. The failure is regarded as a percolation process, and the percolation threshold is taken as the failure indicator in this method. Accordingly, instantaneous availability model of wireless multimedia sensor network is established combining percolation theory and Markov process on the basis of reliability characteristics analysis. By Laplace transformation, the analytic solution is worked out, and one numerical example is given to verify the model comparing with existing studies. Finally, numerical simulation analyses are used to show the impact of different parameters on instantaneous availability, and the ways to suppress fluctuations are obtained by comparing with fluctuation images.

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)0.004
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.271
Teacher spread0.255 · 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