Private Blockchain-Based Wireless Body Area Network Platform for Wearable Internet of Thing Devices in Healthcare
Bibliographic record
Abstract
In recent years, healthcare systems have included the Internet of Things (IoT) technology in their services, such as in remote patient monitoring systems. Wearable IoT devices can provide information regarding the patient's health that are accurate and time-sensitive. However, vulnerabilities are evident as more IoT devices connect to the network. For healthcare services, the security of patient data is an issue. At the same time, with real-time data transmissions, the network runs into manageability concerns. In this work, we propose a private blockchain-based Wireless Body Area Network (WBAN) platform to aid wearable IoT devices in healthcare services. We chose this blockchain technology due to its strengths in security. Then, we enable a distributive architecture using WBANs to introduce a decentralized configuration that can ensure privacy among wearable IoT devices within the network. To evaluate the feasibility of the proposed platform in terms of latency and throughput, we conducted experiments with several wearable IoT devices. The results show that integrating a WBAN to create a fog server improves the network performance with an increasing number of IoT devices and packet size. Also, the blockchain showed its ability to address security threats in healthcare services. We evaluate our proposed platform through a performance test and a STRIDE threat model, and we prove its feasibility in improving the security and manageability of wearable IoT devices in healthcare.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".