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Record W2147495826 · doi:10.1109/iscc.2008.4625679

A reliable synchronous transport protocol for wireless image sensor networks

2008· article· en· W2147495826 on OpenAlexaff
Azzedine Boukerche, Yan Du, Jing Feng, Richard W. Pazzi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceWireless sensor networkKey distribution in wireless sensor networksVisual sensor networkWi-Fi arrayWireless networkComputer networkWirelessReal-time computingMobile wireless sensor networkFixed wirelessTelecommunications

Abstract

fetched live from OpenAlex

Recent advances in image sensing hardware have enabled the development of wireless image sensor networks. Similar to generic wireless sensor networks that collect information from surrounding physical environments as scalar variables, wireless image sensor networks can collect data to generate descriptions of interested targets as images or video streams that, in turn, enhance the userpsilas understanding. However, relaying images over wireless image sensor networks with certain QoS requirements is a challenging task due to the characteristics of wireless channels. In this paper, a novel transport protocol for wireless image sensor networks is presented. This protocol aims at the reliable and synchronous transmission of images from multiple sensor nodes to a sync point. The semantics of JPEG stream are exploited in order to improve the performance of the proposed protocol. We have conducted a series of simulation experiments and reported on their results. Our simulation results show that the proposed protocol performs well in error-prone wireless channels even under high packet loss rates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.251
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations32
Published2008
Admission routes1
Has abstractyes

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