Decentralized and secure delivery network of IoT update files based on ethereum smart contracts and blockchain technology
Bibliographic record
Abstract
The pervasiveness of IoT devices makes the delivery mechanism of security updates a challenge. Current IoT systems rely on centralized or brokered paradigms or clouds with huge computational and storage capacities. The existing centralized IoT setups are therefore expensive as the result of factors such as the high costs associated with cloud server and network infrastructures and maintenance. Thus, the need for a fully decentralized peer to peer and secure technology to overcome these problems rises into the realm of existence. Blockchain provides a solution that fulfills the requirements of such a platform. Ideally, the update infrastructure should implement the CIA triad properties (Confidentiality, Integrity, and Availability). In this article, we study how a blockchain application can meet these requirements and propose a novel system to decentrally distribute digital content in a peer-to-peer network using the blockchain technology and smart contracts to overcome the concerns mentioned above. Additionally, in order to prevent the issues stemming from the free-riding challenge in P2P networks (peers refrain to generously share their resources to distribute updates), we exploit a Nash equilibrium micropayment mechanism to grant adequate incentive for peers to participate in distributing IoT update files.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".