Monetization using Blockchains for IoT Data Marketplace
Bibliographic record
Abstract
The number of Internet of Things devices is growing dramatically, generating a huge amount of data which is becoming a valuable asset for data analysts. This trend culminates towards the creation of an IoT data marketplace, where streams of data from heterogeneous sources are sent in real time to various data consumers and are metered for monetization purposes. Publish/subscribe systems, such as Message Queuing Telemetry Transport (MQTT), are a promising solution to act as a transport layer for real-time data streams in a decoupled and large scale manner. However, pub/sub systems lack two key properties for an IoT data marketplace: (1) it does not provide any monetization logic; (2) it assumes that the pub/sub brokers are trusted entities, which is not the case in a decentralized or federated marketplace setting. In this paper, we address these issues using a reliable and transparent monetization system based on Distributed Ledger Technology (DLT) and smart contracts. We propose three monetization solutions and demonstrate the trade-off between the overhead of tracking IoT data on a blockchain vs. the accuracy of the monetization for data producers and consumers. In particular, we provide a Bloom filter-based solution for efficient verification of data exchange. We implement our system using Ethereum and Solidity and evaluate with respect to contract gas cost.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".