IoT Based Intelligent Home Automation Using Automated Smart Devices
Bibliographic record
Abstract
The concept of “Smart Home” is becoming increasingly popular in recent times. Technological advances in IoT have brought “Smart” Devices capable of much more than just sensing physical parameters. This paper aims to explore and demonstrate the possibility of creating custom made Smart Devices using open-source generic hardware components and integrate the said devices with the pre-existing Smart Devices to implement an IoT based Smart Home environment. In this paper a Smart Home System titled “IoT Based Intelligent Home Automation Using Automated Smart Devices” is designed and implemented. The proposed Smart Home System ((Hereafter referred as SHS)) consists of three Smart Devices out of which one is an Alexa Smart Speaker. Smart Energy Meter (Hereafter referred as SEM / Smart Energy Meter) and Smart Health Tracker (Hereafter referred as SHT) are the other Smart devices in the SHS. SEM is a Smart Energy Meter which can measure the parameters w.r.t Power (Voltage, Current and Energy Consumed). It also has a subsystem which can detect Gas Leakage or Earthquake and automatically trigger a Power Shutdown to prevent any potential damage. SHT is a Smart Health Tracker which can measure parameters w.r.t Health (Heart Rate, SpO2%, Body Temperature). It can also measure Ambient Temperature and Humidity. It is equipped with a Buzzer-LED Warning system to alert the user when the Vitals fall or increase beyond a standard threshold (depending on the Health Parameter considered). All the devices are connected to Blynk Cloud, where the user data generated from both the devices is stored. The user can view the Parameters being measured by the devices on the respective Blynk Dashboards. Alexa Smart Speaker is used to interact with the devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".