A Blockchain-Based Decentralized Composition Solution for IoT Services
Bibliographic record
Abstract
Diversified Internet of Things services are becoming more complex and strictly user-defined. Traditional cloud solutions proved to be both costly in terms of resources and time efficiency. To overcome such a burden, researchers developed fog solutions for faster service responsiveness. Fog-to-Fog communication and cooperation was then introduced to compose services on-the-go for user-specific requests with the aid of mobile edge devices. This paper introduces a blockchain-based decentralized service composition solution for complex multimedia service delivery to cloud subscribers. The proposed work dynamically creates user-defined services without requiring any intermediary service or network provider entities to authenticate and deliver composite services. The composition process uses a reinforcement learning technique to construct secure and reliable composition paths. Participants are rewarded by cloud and fog entities for solving complex composition processes. Simulation results conducted on the system show that by adapting the proposed technique, fog and cloud entities require less resources and reduced power usage with increased service delivery success rates to cloud subscribers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".