A Comprehensive Survey on Lightweight Cryptography Algorithms to Enhance Quality of Service in Medical Internet of Things
Bibliographic record
Abstract
Internet of Things (IoT) refers to the network of interconnected electronic devices and software that gather, transmit, and analyze data.Included in the healthcare category are fitness trackers and other wearable tech, as well as high-tech medical equipment used in hospitals to record patients' vital signs.The Internet of Medical Things (IoMT) enables these devices to communicate and share data, which improves the ability of healthcare providers, nurses, and family members to deliver efficient and timely care.Natural disaster prediction, information management, agriculture, healthcare monitoring, and many more disciplines could potentially benefit from it.No IoT application should ever compromise on security.Security measures to protect sensitive patient data are becoming more important as IoMT devices become more widely used.All the way from high-tech medical imaging systems to portable fitness trackers, these gadgets are constantly gathering and sending patient data in real-time.The security and privacy of sensitive medical data are becoming more important as the IoMT is being used more and more in healthcare.As the IoMT was being developed, the security of user data was given top priority.Lightweight cryptography (LWC) is one approach that might be used to enhance the security and privacy of the IoMT.Execution time, memory consumption, latency, throughput, and security resilience are some of the parameters taken into account when evaluating algorithms on IoT boards with limited processor power and capabilities.Following these steps will help users evaluate cryptographic algorithms for their suitability and make well-informed selections as they search for the best ones that strike a balance between performance and security.This research benefits academics and researchers by providing a deeper understanding of existing security models and facilitating the development of improved approaches for protecting medical IoT data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".