Towards A Scalable DAG-based Distributed Ledger for Smart Communities
Why this work is in the frame
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Bibliographic record
Abstract
In recent years, Distributed Ledger Technology (DLT) has been playing a more and more important role in building trust and security for Internet of Things (IoT). However, the unacceptable performance of the current mainstream DLT systems such as Bitcoin can hardly meet the efficiency and scalability requirements of IoT. In this paper, we propose a scalable transactive smart homes infrastructure by leveraging a Directed Acyclic Graph (DAG) based DLT and following the separation of concerns (SOC) design principle. Based on the proposed solution, an experiment with 40 Home Nodes is conducted to prove the concepts. From the results, we find that our solution provides a high transaction speed and scalability, as well as good performance on security and micropayment which are important in IoT settings. Then, we conduct an analysis and discuss how the new system breaks out the well-known Trilemma, which claims that it is hard for a DLT platform to simultaneously reach decentralization, scalability and security. Finally, we conclude that the proposed DAG-based distributed ledger is an effective solution for building an IoT infrastructure for smart communities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it