IoT-Enabled e-Health Systems: Navigating Security Challenges and Strategic Recommendations
Bibliographic record
Abstract
The Internet of Things (IoT) facilitates the integration of diverse devices for data collection and exchange, significantly impacting various domains, including e-health. E-health systems leverage IoT to monitor patients' health through smart medical devices, enabling local and remote data access. Despite the benefits, the increased connectivity introduces new cybersecurity risks, as malicious actors can exploit vulnerabilities to access sensitive patient information. Traditional security measures have mostly focused on securing individual devices through authentication and encryption. However, many medical devices lack built-in security features or the ability to be updated. To this end, this paper proposes a shift towards system-level security for e-health IoT systems, emphasizing the protection of the entire system rather than just the devices. The paper outlines best practices and recommendations to enhance security, improve interoperability, and address current gaps. These recommendations and guidelines are introduced to support medical institutions, device manufacturers, policymakers, and governments in developing robust security frameworks and policies. The recommendations are designed to be actionable across various levels of the e-health system, fostering secure and interoperable e-health solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".