Developing an Android Application for Internet of Things (IoT) Based Light Control using Android Studio
Bibliographic record
Abstract
Technological advancements are driving the development of home automation systems, one of which is Internet of Things (IoT)-based light control. Popular applications such as Blynk are often used in these systems, but they have limitations in automation flexibility, interface design, and backend control that make further development less than optimal. This research aims to develop an Android application to control IoT-based lights by utilizing the Firebase Realtime Database as a more flexible and independent backend. The system is designed using Android Studio and integrated with the NodeMCU ESP32 and the FC-04 sound sensor to enable both online and offline light control. The research method uses prototyping, which includes creating an initial version of the application, testing, and iterative improvements based on feedback. Testing was conducted using the Arduino IDE for IoT devices, Android Studio for mobile applications, and Black Box Testing to verify system functionality. The test results show that the application is able to control lights in real-time over the internet and can still be controlled using sound sensors when offline. Available features include ON/OFF control, automatic schedule settings, and light status monitoring through Firebase. This system offers a cost-effective, flexible solution that does not rely on third-party platforms, so it has the potential to be further developed to support other smart home systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".