Onboarding and Software Update Architecture for IoT Devices
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 vast number of in-use Internet of Things (IoT) devices is by consensus, expected to continue rapid growth. These devices are subject to an expanding list of attacks that exploit both software vulnerabilities and design choices. This highlights the importance of architectural design of management for cryptographic keys involved in both initial configuration (onboarding) and secure, automatic update of device software and firmware. Low-level IoT devices with constrained processors and smaller registers and caches are computationally challenged to carry out desktop- and server-type public-key cryptographic operations, e.g., as needed for key establishment and authentication of software updates. To this end, we design and prototype an architecture for onboarding and secure software update of low-level IoT devices (8-bit). It uses elliptic curve cryptography (Curve25519), authenticated key establishment, and a known continuity-based key-locking mechanism that uses a public key embedded in a current software image to verify the signature on a software update. We also provide an informal security analysis. The design addresses the scenario of a transfer of update authority, e.g., when a manufacturer ceases to provide ongoing software updates upon going out of business.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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