AFNT: A Secure Data Storage Scheme Based on IOTA Tangle for Wireless Sensor Networks
Bibliographic record
Abstract
Blockchain is a distributed ledger technology that enables cryptocurrencies, such as Bitcoin and Ethereum. However, it suffers from the scalability problem, which means that not all generated blocks can be added to the chain in a timely manner. For the Internet of Things (IoT), which involves a large number of interconnected devices, blockchain is infeasible due to the scalability issue. Instead, a different distributed ledger technology, IOTA Tangle, is expected to be widely adopted in IoT applications. Nevertheless, it has been found that the tip selection and weight update operation of IOTA Tangle could negatively affect its efficiency. To tackle this problem, Fishing Net Topology (FNT) was proposed to completely eliminate the tip selection and weight update operation. However, in our research, we noticed that the width of the fishing net used in FNT remains unchanged, which has a serious impact on the performance of FNT. In this paper, we focus on Wireless Sensor Networks (WSNs), which are often used to implement IoT applications. Specifically, we present a secure data storage scheme based on FNT for WSN, Adaptive FNT (AFNT). Our experimental results indicate that AFNT outperforms FNT in terms of layer number, average fishing net width, percentage of over-approved nodes, and percentage of over-approving nodes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".