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IoT in Coalition Federated Operations: Multi-National C2 Integration and Technical Interoperability Experiments

2023· article· en· W4390189735 on OpenAlexaff
Marco Manso, Fernando Freire, Janusz Furlak, Bárbara Guerra, James Michaelis, Daniel Ota, R. W. Claus, Niranjan Suri, Roberto Fronteddu, Edoardo Di, Konrad Wrona, Emil P. Andersen, Frank T. Johnsen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsInteroperabilityComputer scienceExploitSituation awarenessInternet of ThingsDomain (mathematical analysis)Task (project management)Computer securityWorld Wide WebSystems engineeringEngineering

Abstract

fetched live from OpenAlex

The Internet of Things (IoT) is an emerging and disruptive technology in the military domain. Military technology innovators can exploit the IoT world to promote information superiority in the battlespace through novel and abundant sensory capabilities. Our research was conducted as part of the NATO research task group IST-176 on "Federated Interoperability of Military C2 and IoT Systems" to investigate how IoT data can be ingested into C2 systems in a federated coalition operation. Our approach leverages industrial standard protocols and formats, while including optional fields to convey military-specific unit information along with IoT sensor data. Our experiments demonstrate and validate the approach in which systems from different nations are connected in a federated environment, delivering a high success rate in message delivery and enabling a consistent display of the situational picture across different C2 systems. The results of the experiments demonstrate the feasibility of integrating IoT information in a military information flow, to support collective C2.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.085
GPT teacher head0.341
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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