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A New Model for Cognitive IVT based on loT for Critical Safety Solutions: Firefighter Use Case

Authors: Mohamed Saifeddine Hadj Sassi; Mina Saghafian; Federica Battisti; Marco Carli;

A New Model for Cognitive IVT based on loT for Critical Safety Solutions: Firefighter Use Case

Abstract

Immersive visual technologies and Internet of things are considered the main paradigms in defining new models of immersive training for critical safety applications. Accordingly, matching appropriate devices and technologies with the training requirements and its individual, technological, and operational factors is mandatory to achieve the training goals. In this context, we investigate and classify different factors based on immersive technologies, emerging devices, and sensors. Furthermore, we propose a new model for cognitive immersive visual technologies for critical safety solutions. The proèposed model is validated in a use case of a fire brigade intervention after an earthquake. The use of this model mitigates heterogeneity conflicts due to the variety of immersive technologies for a specific situation by respecting stakeholders’ requirements. This paper helps future researchers, industrial and enterprise stockholders to select adequate immersive visual technologies, devices, and engines for creating further critical safety solutions.

Country
Italy
Keywords

Immersive technologies; IoT; training; virtual reality

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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