Securing RDS broadcast messages for smart grid applications
Bibliographic record
Abstract
Efforts to reduce peak electrical demand have led to the introduction of demand response (DR) programmes for residences. DR programmes allow customers to reduce or shift consumption to off-peak periods in response to price signals. The RDS network is a strong candidate for delivering DR messages due to its low-cost nature and ubiquitous coverage. However, security concerns arise due to the wireless nature of the communication channel.We present evaluations of three candidate cryptographic methods that could be employed to offer source authentication over the RDS network: BiBa, HORSE and elliptic curve digital signature algorithm (ECDSA).We compare the security offered by the protocols, the bandwidth overhead, computational costs and message reception probability. Simulation results show that, up to a distance of 90 km, all authentication schemes do not affect message reception by the receivers. Beyond that, all the schemes have an effect on message reception due to increased message sizes and receiver bootstrapping for BiBa and HORSE. ECDSA and HORSE outperform BiBa in terms of message reception beyond 90 km. ECDSA, however, offers higher security than HORSE and BiBa but at the cost of increased computational complexity, in particular, at the receivers. In addition, has the highest bandwidth overhead.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".