A PHY-Aided Secure IoT Healthcare System with Collaboration of Social Networks
Bibliographic record
Abstract
This paper proposes a novel physical-layer-aided security technique for protecting a social Internet of things (SIoT) architecture-based healthcare system. Exploiting the social relationship link between healthcare user (e.g., patients and elderly people) and healthcare provider (e.g., physicians), social networks can play the role of a trusted online platform to establish service application interfaces between healthcare user (HU) and healthcare provider (HP). This enables the Internet of things (IoT) medical devices (e.g., IoT body sensor) to timely share the bio-data of HU with remote HP via the both storage-rich and computational resource-rich social networks. Given the high security requirement on SIoT data sharing and the fact that resource-constrained IoT devices cannot efficiently execute complicated cryptography, a robust and cost-effective two-phase security method is proposed by exploiting the device-specific physical-layer (PHY) attributes. Specifically, the PHY carrier frequency offset and in-phase/quadrature-phase imbalance of an IoT device are practically estimated to generate the PHY-ID. Using our PHY-ID, the SIoT HU authentication and the bio-data confidentiality are simultaneously enhanced without posing any additional implementation overhead at IoT body sensors, which is especially applicable for the resource-constrained IoT devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".