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 security of data in motion and at rest depends on the ability to exchange session keys between communicating parties. Key agreement approaches can provide the additional security assurance of perfect forward secrecy, however, for many Internet of Things resource-constrained devices the session key establishment process is too costly in terms of energy consumption and processing time. In this paper we quantify the energy consumption and execution load when performing session key establishment. We develop a software security framework, implementing both lightweight key transport and key agreement, the latter based on elliptic curve Diffie-Hellman. Measurements are taken using energy and digital-events monitoring tools. We find that key agreement implemented via software requires a quantity of energy thousand of times greater than a key transport approach. Also, we measure and quantify how much a hardware implementation can improve energy and execution time performance. Our research provides critical information for practitioners in selecting the appropriate hardware and security scheme for IoT applications.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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