An energy efficient real-time vehicle tracking system
Bibliographic record
Abstract
Internet of Things (IoT) is a growing technology that combines and connects a variety of devices to generate more beneficial information. In this paper, an example of IoT portable system has been built and tested, which is called Smart Vehicle System (SVS). SVS includes three main parts: the Tracking Unit, Cloud, and Android application. The Tracking Unit is positioned inside a vehicle to sense the vehicle's temperature, speed, and location then uploads them to the cloud via a GSM network. Components and communication of the SVS are described in detail. constraints are included in the system to notify the administrator and the driver of certain events. SVS is a portable system, whose operation depends on batteries; therefore, a power reduction algorithm is proposed and examined. We have performed 19 different experiments, before and after applying the proposed algorithm, four of them are dynamic and 15 are at a fixed location. With the power reduction algorithm, we are able to reduce the energy consumption of the tracking unit by up to 17%.
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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 | 0.000 |
| 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".