Sensing instrumentation using smartphones: Securing impact and awareness
Bibliographic record
Abstract
As the Internet of Things (IoT) continues to proliferate though the field of instrumentation and measurement, there are many issues to address: one being security. The very technologies that are utilized by advanced IoT algorithms can also be compromised and provide access to a user's smart phone sensors. In this paper, algorithms that activate sensing instrumentation in conjunction with Near-Field Communication (NFC), Quick Response (QR) codes, and HTML 5 scripting on smartphones are presented, and examined from a security perspective in terms of vectors that could result in unwanted access of sensor data, and the algorithms to derive these parameters. The IoT proliferation issue of security is addressed in this paper by examining sensor data access. With no user prompt at all, accelerometer and gyroscope data can be extracted, and from this, the user's speed and position changes can be extracted. Motion data then can be accessed with nothing other than activation using the tap of an NFC card, QR code scanned, or URL entered. With a tap and a single user acknowledgment, images can be captured or locations can be shared. New instrumentation and measurement techniques and IoT proliferation opens up many new opportunities, but regular security awareness is necessary for steps to increase protection from unwanted sensor data leakage. When designing an IoT instrumentation system, it is important to consider user security awareness, training, and overall situational awareness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".