Mobile wireless RSA overlay network as critical infrastructure for national security
Bibliographic record
Abstract
The article presents an analysis on the use of wireless sensor networks for safeguarding our critical infrastructures and the management of a disaster for first response emergency scenarios. This analysis is based on a qualitative comparison of the features of a wireless sensor network to a list of requirements defined by The National Security Telecommunications Security Convergence Task Force report for the national security and emergency preparedness (NS/EP) of the USA. The result is that a wireless sensor network can only meet part of these requirements and therefore a more complex network that supports an overlay of mobile and fixed wireless networks, existing networking infrastructure, and sensor/robotic Web services is required. A sensor network architecture is presented that leverages the IEEE 1451 sensor model within a wireless mesh network in order to facilitate access to the sensory data, and communication between mobile robotic sensing agents (RSAs). In order to distribute the sensed data reliably to experts around the world, these highly mobile emergency response sensor networks need to leverage existing enterprise network access points. The emergency sensor mesh network and the enterprise host form a symbiotic relationship so that the sensory data can be made available to users through the host's access to the Internet.
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".