Enhancing IoT Network Data Monetization with Blockchain: A Decentralized Approach Using Ethereum Blockchain Contract
Bibliographic record
Abstract
Blockchain (BLCH) has emerged as a transformative technology for securing and decentralizing data transactions in the Internet of Things (IoT).By leveraging a distributed and incontrovertible record, BLCH enables trustless and transparent interactions among IoT nodes, ensuring security, traceability, and real-time verification.Given the diverse nature of IoT devices from sensors to actuators blockchain provides a robust framework for secure and decentralized data exchange, eliminating the need for centralized intermediaries.This paper explores the monetization of IoT network data using blockchain, proposing a decentralized application (DCAPP) built on the Ethereum blockchain.Our approach employs Blockchain Contracts BLCOs to facilitate secure and automated transactions in a Sensing-as-a-Service (SaaS) marketplace, where IoT sensor data can be bought and sold efficiently.By integrating blockchain, our model enhances data accessibility, trust, and scalability, addressing key challenges in security and interoperability.In this paper, we examine BLCH possible to redesign IoT systems, concrete the technique for safe and incentivized data-driven claims.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".