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Record W1517447645 · doi:10.1109/icit.2015.7125384

Wi-Fi-based hierarchical Wireless Networked Control Systems

2015· article· en· W1517447645 on OpenAlexaff
Esraa A. Makled, Hassan H. Halawa, Ramèz M. Daoud, Hassanein H. Amer, Tarek K. Refaat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Time Synchronization Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSupervisorActuatorNode (physics)Network packetComputer scienceWirelessController (irrigation)Interference (communication)HierarchyComputer networkControl systemControl theory (sociology)Control (management)EngineeringTelecommunicationsChannel (broadcasting)Electrical engineering

Abstract

fetched live from OpenAlex

This paper proposes a novel architecture for a hierarchical Wireless Networked Control System (WNCS). It consists of three cascaded workcells each containing 30 sensors, 30 actuators and one controller. The wireless communication protocol used is IEEE 802.11g with multicasting. The hierarchy of the system is such that the lowest level is that of the sensors and actuators, the intermediate level is the controllers, and the highest level is a supervisory node. This supervisor can be either active or passive. System performance is measured using OPNET simulations and the results are confirmed analytically. The system is shown to tolerate all possible controller failure scenarios. The supervisor can handle the entire control load of all three controllers, should the need arise. The system exhibits zero packet drops and delay constraints are met in all scenarios. The effect of interference is then investigated and the maximum interference that can be tolerated by the system is quantified.

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)0.000
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.223
Teacher spread0.206 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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Same topicNetwork Time Synchronization TechnologiesFrench-language works237,207