Reference Security Architecture for Body Area Networks in Healthcare Applications
Bibliographic record
Abstract
Body Area Network (BAN) and Wireless Body Area Network (WBAN) are being used in the healthcare industry to improve medical outcomes by monitoring and treating patients while they go about their everyday lives. BAN facilitates data collection from the human body via a small wearable or implantable sensor. This technology has improved the quality of medical services provided and lowered some associated costs. BAN has a wide range of applications such as monitoring patients' medical conditions and enhancing their response to treatment plans, but at the same time security and privacy are among major concerns in BAN-based healthcare systems as the patients' data must be kept secure from adverse events and attackers during transmission and in storage. This paper reviewed BAN communication standards, security threats and vulnerabilities to BAN - based systems as well as existing security and privacy mechanisms. Based on the review the paper proposed a reference security architecture which focuses on developing a secure foundation of the BAN layer called Tier 1. The reference security architecture incorporates IEEE802.15.6 (WBAN) standard, which provides a security baseline. The architecture will assist BAN manufacturers and auditors to develop and ensure secure BAN.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".