Decentralized and secure delivery network of IoT update files based on ethereum smart contracts and blockchain technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it