Recent Trends of Integration of Blockchain Technology With the IoT by Analysing the Networking Systems: Future Research Prospects
Bibliographic record
Abstract
In recent times, attention has surged towards entities with the potential to revolutionize various sectors. The integration of Internet of Things (IoT) and blockchain technologies, known as IoT-blockchain, offers numerous advantages, including heightened security, privacy, traceability, transparency, and reduced costs. This abstract delves into the taxonomy and prominent platforms of blockchain applications for IoT in networking systems, exploring recent advancements, obstacles, and future research avenues. IoT blockchain's crucial aspect lies in establishing decentralized networks, enabling secure collaboration and data interchange among diverse devices without a central governing entity. Platforms like Ethereum, Hyperledger, and IOTA facilitate the creation and management of these networks. Recent developments focus on enhancing security, scalability, and efficiency through novel consensus mechanisms and cryptographic techniques. Challenges persist, including the need for improved interoperability, integration with existing systems, efficient governance, regulatory structures, and the identification of use cases and business models for widespread adoption. The examination of successful governance, regulatory frameworks, and potential adoption catalysts completes the discourse on IoT blockchain technology.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".