The platform will undergo maintenance on Sep 14 at about 7:45 AM EST and will be unavailable for approximately 2 hours.
2021
DOI: 10.3390/s21175790
|View full text |Cite
|
Sign up to set email alerts
|

Enabling Next-Generation Public Safety Operations with Mission-Critical Networks and Wearable Applications

Abstract: Public safety agencies have been working on the modernization of their communication networks and the enhancement of their mission-critical capabilities with novel technologies and applications. As part of these efforts, migrating from traditional land mobile radio (LMR) systems toward cellular-enabled, next-generation, mission-critical networks is at the top of these agencies’ agendas. In this paper, we provide an overview of cellular technologies ratified by the 3rd Generation Partnership Project (3GPP) to e… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
2
1
1
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 20 publications
0
2
0
Order By: Relevance
“…While some works, such as (Renda et al, 2021;Marcu et al, 2023;Barnard et al, 2022) aim to understand the decisions of ML models, others, such as (Terra et al, 2020;Morichetta et al, 2019;Spyrou and Kappatos, 2023) also uses XAI methods to explain relationships between network variables, either for cognitive purposes or for selecting relevant attributes when training ML models. Additionally, XAI constitutes a fundamental tool for regulatory compliance by facilitating traceability and explanation of AI decisions, something essential in critical applications such as healthcare, autonomous transportation, and cybersecurity management in these next-generation networks (Saafi et al, 2021). For example, the European Union's General Data Protection Regulation (GDPR) requires that AI models that make decisions about individuals be explainable (Bayamlıoglu, 2022).…”
Section: Introductionmentioning
confidence: 99%
“…While some works, such as (Renda et al, 2021;Marcu et al, 2023;Barnard et al, 2022) aim to understand the decisions of ML models, others, such as (Terra et al, 2020;Morichetta et al, 2019;Spyrou and Kappatos, 2023) also uses XAI methods to explain relationships between network variables, either for cognitive purposes or for selecting relevant attributes when training ML models. Additionally, XAI constitutes a fundamental tool for regulatory compliance by facilitating traceability and explanation of AI decisions, something essential in critical applications such as healthcare, autonomous transportation, and cybersecurity management in these next-generation networks (Saafi et al, 2021). For example, the European Union's General Data Protection Regulation (GDPR) requires that AI models that make decisions about individuals be explainable (Bayamlıoglu, 2022).…”
Section: Introductionmentioning
confidence: 99%
“…On the application side, we observe that public safety organizations have already begun to shift from traditional land mobile radio to cellular communications systems, leveraging a new set of deployed devices to meet mission-critical requirements and target new public-safety broadband applications. Accordingly, 3GPP Rel-16 targets defining the common architecture for public safety and commercial ProSe services [98]. In the case of public safety, maintaining ProSe discovery and communication is especially critical when the UE resides outside the coverage area of the cellular network, e.g., in the case of disaster management in remote areas.…”
Section: Motivationmentioning
confidence: 99%