Body sensor network encryption and team user authentication scheme based on electrocardiogram detector
Bibliographic record
Abstract
Summary Body sensor network (BSN)/wireless body area networks (WBANs) is one of the essential aspects of the Internet of Things (IoT), which is possible to track the patient's essential biological and cognitive data through wearable sensors. The WBAN is easily accessible due to open sources; data security and privacy should be maintained throughout the communication characteristics. Due to the advancement of technology, BSN is restricted in a wide range of clinical applications. BSN is used to monitor the physiological characteristics continuously. The data can be easily traced by the attackers where the fingerprint scanners obtained can be used for the user authentication because of its wireless communication. This paper proposes a body sensor network encryption and user authentication (BSN‐EUA) scheme based on the ECG detector. BSN‐EUA scheme is used to provide the fingerprint scanners for user authentication, and all the activities regarding the health of a person are recorded on the mobile phone. The electrocardiogram's (ECG) descriptive characteristics are used throughout the authentication process as a recognizable fingerprint parameter. Quick community security protocols are then provided across all certified sensors where minor adjustments are needed to update the key generation mechanism on the sensor's side. Evaluation results show that the methodology suggested could reach the required security requirements and avoid several threats.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".