On the Use of Biometrics to Secure Wireless Biosensor Networks
Bibliographic record
Abstract
The fast improvements in a variety of technologies such as microprocessing, sensing material, and most importantly wireless technology resulted in development of the wireless sensor network technology. Wireless sensors that can be either implantable inside the human body or wearable by individuals are called the wireless biosensors. The wireless biosensors are used to gather real time and continuous medical data from different parts of the human beings. This medical data is typically sent to an external sensor and then to its associated destination where data processing and a final decision is carried out. Due to nature of medical data and their usage, ensuring the security of this data is extremely important. There are several limitations associated with biosensor networks such as limitation in power, memory, computation capability, and communication rate which makes the wireless biosensor security a real challenging problem. These security challenges form substantial barriers for the wide adoption of the technology. Biometrics approach is an efficient way to overcome the insecurity of the wireless biosensor networks. In this paper, we will look at how biometrics has helped securing data in wireless biosensor networks, and present the remaining challenges to have a workable biometric-based security framework for wireless biosensor networks.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".