Hybrid Lightweight Cryptographic Framework for Enhancing Security and Efficiency in Healthcare Wireless Sensor Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) are integral to Healthcare IoT (H-IoT) for continuous patient monitoring, yet they face constraints in energy, computation, and memory while requiring strong security.This paper introduces a hybrid lightweight cryptographic framework that integrates symmetric ciphers with authenticated encryption to achieve an optimal balance between performance and protection.The framework was evaluated through simulations and hardware experiments, measuring encryption latency, energy usage, memory footprint, and resilience against replay and man-in-the-middle (MITM) attacks.The results reveal the improvement in security with low resource overhead using ASCON128 cipher and achieved better efficiency reduce the encryption time by 25% and energy consumption by 30% even it requires more resources.This proposed hybrid architecture improves gateway node security, finally the proposed healthcare WSN proved safe, energy efficient and scalable according to the proposal architectural farmwork.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it