Public Key Reinforced Blockchain Platform for Fog-IoT Network System\n Administration
Bibliographic record
Abstract
The number of embedded devices that connect to a wireless network has been\ngrowing for the past decade. This interaction creates a network of Internet of\nThings (IoT) devices where data travel continuously. With the increase of\ndevices and the need for the network to extend via fog computing, we have\nfog-based IoT networks. However, with more endpoints introduced to it, the\nnetwork becomes open to malicious attackers. This work attempts to protect\nfog-based IoT networks by creating a platform that secures the endpoints\nthrough public-key encryption. The servers are allowed to mask the data packets\nshared within the network. To be able to track all of the encryption processes,\nwe incorporated the use of permissioned blockchains. This technology completes\nthe security layer by providing an immutable and automated data structure to\nfunction as a hyper ledger for the network. Each data transaction incorporates\na handshake mechanism with the use of a public key pair. This design guarantees\nthat only devices that have proper access through the keys can use the network.\nHence, management is made convenient and secure. The implementation of this\nplatform is through a wireless server-client architecture to simulate the data\ntransactions between devices. The conducted qualitative tests provide an\nin-depth feasibility investigation on the network's levels of security. The\nresults show the validity of the design as a means of fortifying the network\nagainst endpoint attacks.\n
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".