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Low-Cost ESP32, Raspberry Pi, Node-Red, and MQTT Protocol Based SCADA System

2020· article· en· W3092326279 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

Venue2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) · 2020
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMQTTSCADAComputer scienceMessage queueHome automationComputer networkNode (physics)Embedded systemEnergy consumptionAutomationReal-time computingTelecommunicationsInternet of ThingsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Growing energy cost and demand has motivated many organizations to achieve smart ways to monitor, control, and save energy. Smart automation can reduce costs while still satisfying energy demand. The residential, commercial, and industrial sectors can utilize the technologies of the Internet of Things (IoT) to manage energy consumption better. This paper presents a low-cost, open-source, and reliable Supervisory Control and Data Acquisition (SCADA) system for home monitoring and control system. The presented SCADA system consists of analog sensors, ESP32, Node-RED, and Message Queuing Telemetry Transport (MQTT) through local Wi-Fi to remotely access and control appliances. This system helps the users to monitor various conditions in the home, such as temperature, humidity, pressure, and light intensity. Thus, users can remotely monitor various devices such as lights, fans, heating/cooling systems, make decisions based on the feedback of sensors.

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)0.001
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.712
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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