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Explainable semantic wireless anomaly characterization for digital twins
ID
Bertalanič, Blaž
(
Avtor
),
ID
Hanžel, Vid
(
Avtor
),
ID
Fortuna, Carolina
(
Avtor
)
PDF - Predstavitvena datoteka,
prenos
(3,43 MB)
MD5: A485DD0CC308C6283C8A19A7E23B9071
URL - Izvorni URL, za dostop obiščite
https://www.sciencedirect.com/science/article/pii/S1389128624004924
Galerija slik
Izvleček
The shift toward software-centric network infrastructures is driven by the increasing need for networks to be responsive, flexible, and scalable in the face of an expanding set of connected devices. The digital twin (DT) approach, mirroring physical entities in a digital format, has emerged as a key enabler of network reliability and availability. Incorporating artificial intelligence (AI) into DTs enhances the resilience of networks by providing in-depth analysis and increasingly automated mitigation strategies against operational disruptions. In this work, we propose a new AI-based information extraction module that is part of the DT Monitoring component able to processes RSS data, extract and characterize abrupt anomalies. The output of this component is used to maintain an anomaly history in the Link Abstraction within the DT and subsequently inform possible automatic mitigation actions. We design the AI-based information extraction module to identify and characterize three types of RSS based anomalies. Our extensive performance analysis on 10 versions of the ”You Only Look Once” architecture reveals that YOLOv8n produces a good tradeoff between performance and computational complexity. We show that our approach performs on par with the state of the art for anomaly detection, while enabling anomaly characterization by location, duration, and severity. By employing two SotA explainability algorithms, we also provide insights into the important regions of the input that trigger the selected model’s classification and characterization decisions.
Jezik:
Angleški jezik
Ključne besede:
wireless network
,
anomaly characterization
,
digital twin
,
explainable model
,
monitoring
,
maintenance
,
wireless anomaly detection
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FE - Fakulteta za elektrotehniko
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Leto izida:
2024
Št. strani:
13 str.
Številčenje:
Vol. 251, art. 110660
PID:
20.500.12556/RUL-159823
UDK:
004
ISSN pri članku:
1872-7069
DOI:
10.1016/j.comnet.2024.110660
COBISS.SI-ID:
202453251
Datum objave v RUL:
26.07.2024
Število ogledov:
146
Število prenosov:
31
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Objavi na:
Gradivo je del revije
Naslov:
Computer networks
Založnik:
Elsevier
ISSN:
1872-7069
COBISS.SI-ID:
23281413
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Projekti
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
P2-0016
Naslov:
Komunikacijska omrežja in storitve
Financer:
EC - European Commission
Program financ.:
HE
Številka projekta:
101096456
Naslov:
An Artificial Intelligent Aided Unified Network for Secure Beyond 5G Long Term Evolution
Akronim:
NANCY
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