On-demand key distribution for body area networks for emergency case
Bibliographic record
Abstract
Recent growth in the Wireless Sensor Networks (WSNs) has given rise to development of new and innovative medical applications. WSNs have occupied the medical area with immense benefits in reducing healthcare costs, doctor-patient efficiency, and enhanced openness for patients and healthcare professionals. Such sensors can be placed in or over a patient's body, forming a Body Area Network (BAN), to monitor the patient's health and vitalities. In these health monitoring applications, sensitive personal information is being transmitted over a network, thus, security needs to be embedded into the system from the start to guarantee privacy of the personal medical information. However, information security incurs additional overhead to the already resource-constrained BANs. This paper aims to provide a resource-efficient, scalable, and lightweight key management algorithm for cases of medical emergency, where a patient requires immediate medical assistance. The proposed key management algorithm securely establishes a key between a patient and the medical staff with minimal overhead without any prior knowledge of one another. The results show that the proposed scheme scales well with increasing number of nodes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".