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Connecting the Battlespace: C2 and IoT Technical Interoperability in Tactical Federated Environments

2022· article· en· W4317928090 on OpenAlex
Marco Manso, Janusz Furtak, Bárbara Guerra, James Michaelis, Daniel Ota, Niranjan Suri, Konrad Wrona

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) · 2022
Typearticle
Languageen
FieldComputer Science
TopicNetwork Security and Intrusion Detection
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsBattlespaceInteroperabilitySituation awarenessComputer scienceInternet of ThingsComputer securityBattlefieldAbu dhabiTask (project management)EngineeringWorld Wide WebSystems engineering

Abstract

fetched live from OpenAlex

The Internet of Things (IoT) has become one of the defining technology trends of the last decade. It has also attracted the attention of military technology innovators as a means to gain information dominance in the battlespace through improved situational awareness. Conducted as part of the NATO research task group IST-176 on “Federated Interoperability of Military C2 and IoT Systems”, this research investigates a secure approach to connect heterogeneous assets that rely on widely used and standardized technologies. To demonstrate the approach, a set of planned experiments is presented in which systems from different nations are connected in a federated environment. The results of the experiments aim to demonstrate the feasibility of integrating battlefield assets, including soldier systems and IoT devices, 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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0040.006
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.262
Teacher spread0.230 · 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