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Record W2105470729 · doi:10.1109/isspit.2009.5407585

Flooding Zone Control Protocol (FZCP): enhancing the reliability of real-time multimedia delivery in WSNs

2009· article· en· W2105470729 on OpenAlexaff
Tarik Elamsy, Randa El-Marakby

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer scienceNetwork packetFlooding (psychology)Computer networkPacket lossReal-time computingWireless sensor networkOverhead (engineering)Redundancy (engineering)Efficient energy useInitializationNetwork congestionEmbedded systemEngineering

Abstract

fetched live from OpenAlex

The flooding zone initialization protocol (FZIP) was proposed as a mechanism to provide power efficient flooding for real-time multimedia data over wireless sensor networks (WSNs). FZIP can initialize different FZ sizes with different performance levels (i.e. loss rate, latency, and overhead). Increasing the FZ size increases redundancy which in turn reduces packet loss but consumes extra power overhead. However, how to choose a suitable FZ size poses a tradeoff between packet loss rate and power efficiency under different network sizes, densities, and radio channel conditions. The static FZ size estimation under dynamic WSNs' conditions leads to unnecessary power overhead or fail to deliver good quality resulting in high packet loss rate. In this paper, we propose the flooding zone control protocol (FZCP) to overcome this problem. FZCP monitors the incoming multimedia packets to detect performance deterioration and change the FZ size (increase/decrease) accordingly. Simulation results show that FZCP enhances the flooding performance and delivers and maintains good quality of real-time multimedia sessions with low energy overhead.

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)0.001
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 score0.495
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.231
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

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
Published2009
Admission routes1
Has abstractyes

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