A Lightweight Blockchain to Secure Data Communication in IoT Network on Healthcare System
Bibliographic record
Abstract
The burgeoning domain of the Internet of Things (IoT) encompasses a myriad of interconnected devices tasked with the automated collection of sensitive data.A paramount challenge within this realm is the establishment of stringent security protocols to safeguard sensitive information and thwart unauthorized access.Although various strategies have been conceived and implemented to fortify data protection in IoT environments, the unique resource limitations intrinsic to IoT devices necessitate further exploration.The criticality of efficient time and memory management for the optimization of IoT application performance cannot be overstated.This paper elucidates the efficacy of employing lightweight blockchain technology as a bulwark to secure numerous IoT applications.It introduces a symmetric cryptographic algorithm, known as Blowfish, tailored for the secure transmission of data within IoT networks.A novel key generation phase has been developed, demonstrating an adept utilization of time and memory resources on IoT devices for the encryption of transaction data.Furthermore, these transactions are recorded within a blockchain database, capitalizing on its inherent immutability.Comparative analysis reveals that the proposed scheme surpasses contemporary algorithms, including AES and 3DES, with regard to encryption time and memory overhead for key generation.This advancement heralds a significant stride in the quest to bolster IoT security.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".