Smart IoT based Accident Monitoring and Rescue System
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.
Bibliographic record
Abstract
Today's drivers face a significant danger of vehicle accidents; hence, it is critical to devise innovative strategies to mitigate their impact and expedite emergency responses, especially considering the significant mortality rate. The proposed Internet-based Automatic Accident Detection and Rescue System (IoT-ADRS) optimizes accident detection and rescue operations by leveraging Internet of Things (IoT) technology. The framework combines several sensors, such as accelerometers, gyroscopes, ultrasonic sensors, GPS modules, and MQ3 sensors, to precisely identify accidents in real time. The Arduino microcontroller analyzes data from several sensors to identify accidents. The system employs GSM modules to transmit critical information, including the exact position and time of the event, to emergency services in real time upon detecting an accident. It examines key components and their connections to gather important data, quickly sound alarms, and manage resources efficiently. This comprehensive study shows that IoT-ADRS improves accident response protocols, saves lives, and reduces the severity of road injuries.
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 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 | 0.000 | 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 | 0.000 | 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 it