Design of an IoT-Based Road Condition Monitoring System Using NodeMCU, Ultrasonic Sensor, and Vibration Sensor
Bibliographic record
Abstract
This study designs an Internet of Things (IoT)-based road condition monitoring system using NodeMCU ESP8266, the HC-SR04 ultrasonic sensor, and the SW-420 vibration sensor. The system is designed to detect road damage such as potholes and uneven surfaces by combining distance measurement and vibration detection, then transmitting the data in real time to the Blynk application. The research employs a prototyping method and is tested on a remote-controlled (RC) car equipped with sensors, which is operated on a simulated track with varying road surface conditions. The test results demonstrate that the system is capable of detecting changes in road surface elevation and vibrations with high accuracy, and transmitting the data to the Blynk application with a delay of less than two seconds. This system has proven effective as a fast, accurate, and integrated tool for monitoring road conditions to support road maintenance and improve road user safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".