Secure Cloud-Based Provisioning and Managing the Complete Life Cycle of IoT Metals
Bibliographic record
Abstract
TIPS (Toronto Infrastructure Provisioning System) is introduced as a new cloud-based IoT metal provisioning and life-cycle management system. TIPS splits the life cycle of IoT metals (devices) into three provisioning phases. In Phase 1, an IoT metal undergoes a tethered hardware provisioning process that moves the non configured metal hardware from its raw state to a configured state (i.e., having a full-fledged operating system). In Phase 2, TIPS provides the configured IoT metal with subsequent provisioning of the software packages and binaries that are specifically intended to configure the metal for a given IoT system task (e.g., data processing, sensing, and actuating). In Phase 3, TIPS continues to provide the now-functioning IoT metal with more software updates such as program upgrades and security patches to meet the metal’s ongoing software needs. TIPS multi-phase provisioning model improves the security of IoT metal provisioning by authenticating and authorizing IoT metals before each provisioning phase, reducing the impacts of security threats like unauthorized OS installation and mass deployment of malicious software stacks on IoT metals. TIPS offers granular control over the IoT metal provisioning process that enhances the timeliness of IoT provisioning systems for reacting to changes in security requirements. TIPS multi-phase provisioning model only marginally increases the provisioning time and energy consumption of IoT metals during the provisioning process while offering many security benefits.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".