Exploring IoT and Blockchain: A Comprehensive Survey on Security, Integration Strategies, Applications and Future Research Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rise of the Internet of Things (IoT) has driven significant advancements across sectors such as urbanization, manufacturing, and healthcare, all of which are focused on enhancing quality of life and stimulating the global economy. This survey offers an in-depth analysis of the integration of blockchain technology with IoT, addressing aspects such as architectural alignment, applications, security, limitations, scalability, and latency. Moreover, this survey focuses on security, integration techniques, and future research directions. The primary contributions of this review include a taxonomy of security concerns specific to IoT, an analysis of integration methods, and insights into consensus mechanisms suitable for resource-constrained environments. These findings highlight the unique challenges and opportunities in IoT–blockchain integration, providing a foundation for advancing secure and scalable IoT applications. By exploring consensus mechanisms and resource-constrained deployments, this paper provides a framework for developing secure and efficient IoT applications utilizing blockchain technology and providing a basis for future research and practical applications. In addition, this survey investigates innovative trends, including AI-driven blockchain for IoT.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it