Privacy-preserving smart grid traceability using blockchain over IoT connectivity
Bibliographic record
Abstract
Smart grid (SG) technology comes with various advantages, among others, of efficient power distribution, reduction in cost consumption, maintain energy loss, and peak quality level in its supply chain. SG aims to provide all required and advanced features to overcome the issues of the existing power grid and to fulfill the rapidly growing demands of power generation, transmission, distribution, and monitoring energy consumption. However, SG has been facing various challenges to ensure the nature of every transaction, transaction verification, and recording in the power supply chain, especially in the context of a large scale IoT(Internet of things) network. Another issue is maintaining the privacy of every transaction, as well as the privacy of participating entities in the power supply chain. In this paper, we propose a blockchain-based proof-of-concept solution to manage every transaction that occurs in an IoT-aided smart grid system. Our solution, IoT-aided smart grid system with blockchain, provides an immutable transaction record, which is always shared and transparent to each participant of the system. Transactions are carried through IoT objects connected in a smart grid, and are recorded, verified, and validated on a blockchain immutable ledger. Furthermore, to verify and maintain the privacy of participants, cryptographic pseudonyms are used by each participant to interact with the SG supply chain, without revealing personal identities and important private information of the participants to malicious entities in the system.
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.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".