Agile IoT for Critical Infrastructure Resilience: Cross-Modal Sensing As Part of a Situational Awareness Approach
Bibliographic record
Abstract
Internet of Things (IoT) continues proliferation. Hospitals, energy industry, power grid, food and water, transportation, etc., are critical asset resources. Critical systems must remain steadfast and reliable. This IoT evolution, sometimes termed, industrial IoT, is significant for critical infrastructure. In harsh climates, human lives can depend daily on these systems functioning well. Better situational awareness can improve resilience of these critical infrastructures and make them increasingly robust. In this paper, the method of agile IoT for critical infrastructure resilience is presented, and an agile IoT model is presented to use signal processing middleware to enable existing sensors commonly deployed in critical infrastructure to be agilely repurposed to: 1) add parallel communication methods and/or 2) sense additional mechanical/physical parameters using existing hardware. The results of the proposed method are applied to use common temperature sensors to detect nontemperature parameters: fluid flow in a pipe (manufacturing or water), ice buildup (transportation or energy infrastructure), and of mechanical door state (health venues of life-saving medicine stored in hospital refrigerators).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".