The AI-driven Permissioned Blockchain System for the Services of Future Internet of Things
Bibliographic record
Abstract
Recently, the Internet of Things (IoT) and blockchain technology have attracted more and more attention. However, given the characteristics of the current blockchain itself, many important issues prevent it from becoming a general platform for IoT to deploy large-scale services. The main problem is how to ensure the scalability of the blockchain system in the dynamically changing IoT scene. Despite numerous studies conducted to address this issue, they failed to account for the dynamic changes in IoT nodes, specifically the addition and deletion of nodes. Instead, they simply opted for a consensus protocol as the presumed optimal solution. In this paper, we employ a permissioned blockchain framework designed for current IoT services. In response to the Quality of Service (QoS) requirements of different IoT nodes, we propose different consensus protocols for different service qualities in the existing IoT architecture, aiming to enhance the adaptability of the blockchain to the different needs of users in the IoT system. First, we quantified several permissioned consensus protocols. Furthermore, IoT services require the selection of block producers possessing significant computing resources, along with the dynamic allocation of network bandwidth specifically for the blockchain. We present an approach where we formulate the joint optimization problem of the selection of consensus mechanism and block producer, and the allocation of available bandwidth. We employ the Dueling Deep Q-network (DDQN) method as a solution for this problem.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".